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Exploring Kenya’s Inequality
A PUBLICATION OF KNBS AND SID
© 2013 Kenya National Bureau of Statistics (KNBS) and Society for International Development (SID)
ISBN – 978 - 9966 - 029 - 18 - 8
With funding from DANIDA through Drivers of Accountability Programme
The publication, however, remains the sole responsibility of the Kenya National Bureau of Statistics (KNBS) and the Society for International Development (SID).
Written by: Eston Ngugi
Data and tables generation: Samuel Kipruto
Paul Samoei
Maps generation: George Matheka Kamula
Technical Input and Editing: Katindi Sivi-Njonjo
Jason Lakin
Copy Editing: Ali Nadim Zaidi
Leonard Wanyama
Design, Print and Publishing: Ascent Limited
All rights reserved. No part of this publication may be reproduced, stored in a retrieval system or transmitted in any form, or by any means electronic, mechanical, photocopying, recording or otherwise, without the prior express and written permission of the publishers. Any part of this publication may be freely reviewed or quoted provided the source is duly acknowledged. It may not be sold or used for commercial purposes or for profit.
Kenya National Bureau of Statistics
P.O. Box 30266-00100 Nairobi, Kenya
Email: [email protected] Website: www.knbs.or.ke
Society for International Development – East Africa
P.O. Box 2404-00100 Nairobi, Kenya
Email: [email protected] | Website: www.sidint.net
Published by
iii
Pulling Apart or Pooling Together?
Table of contents Table of contents iii
Foreword iv
Acknowledgements v
Striking features on inter-county inequalities in Kenya vi
List of Figures viii
List Annex Tables ix
Abbreviations xi
Introduction 2
Isiolo County 9
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Exploring Kenya’s Inequality
A PUBLICATION OF KNBS AND SID
ForewordKenya, like all African countries, focused on poverty alleviation at independence, perhaps due to the level of
vulnerability of its populations but also as a result of the ‘trickle down’ economic discourses of the time, which
assumed that poverty rather than distribution mattered – in other words, that it was only necessary to concentrate
on economic growth because, as the country grew richer, this wealth would trickle down to benefit the poorest
sections of society. Inequality therefore had a very low profile in political, policy and scholarly discourses. In
recent years though, social dimensions such as levels of access to education, clean water and sanitation are
important in assessing people’s quality of life. Being deprived of these essential services deepens poverty and
reduces people’s well-being. Stark differences in accessing these essential services among different groups
make it difficult to reduce poverty even when economies are growing. According to the Economist (June 1, 2013),
a 1% increase in incomes in the most unequal countries produces a mere 0.6 percent reduction in poverty. In the
most equal countries, the same 1% growth yields a 4.3% reduction in poverty. Poverty and inequality are thus part
of the same problem, and there is a strong case to be made for both economic growth and redistributive policies.
From this perspective, Kenya’s quest in vision 2030 to grow by 10% per annum must also ensure that inequality
is reduced along the way and all people benefit equitably from development initiatives and resources allocated.
Since 2004, the Society for International Development (SID) and Kenya National Bureau of Statistics (KNBS) have
collaborated to spearhead inequality research in Kenya. Through their initial publications such as ‘Pulling Apart:
Facts and Figures on Inequality in Kenya,’ which sought to present simple facts about various manifestations
of inequality in Kenya, the understanding of Kenyans of the subject was deepened and a national debate on
the dynamics, causes and possible responses started. The report ‘Geographic Dimensions of Well-Being in
Kenya: Who and Where are the Poor?’ elevated the poverty and inequality discourse further while the publication
‘Readings on Inequality in Kenya: Sectoral Dynamics and Perspectives’ presented the causality, dynamics and
other technical aspects of inequality.
KNBS and SID in this publication go further to present monetary measures of inequality such as expenditure
patterns of groups and non-money metric measures of inequality in important livelihood parameters like
employment, education, energy, housing, water and sanitation to show the levels of vulnerability and patterns of
unequal access to essential social services at the national, county, constituency and ward levels.
We envisage that this work will be particularly helpful to county leaders who are tasked with the responsibility
of ensuring equitable social and economic development while addressing the needs of marginalized groups
and regions. We also hope that it will help in informing public engagement with the devolution process and
be instrumental in formulating strategies and actions to overcome exclusion of groups or individuals from the
benefits of growth and development in Kenya.
It is therefore our great pleasure to present ‘Exploring Kenya’s inequality: Pulling apart or pooling together?’
Ali Hersi Society for International Development (SID) Regional Director
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Pulling Apart or Pooling Together?
AcknowledgementsKenya National Bureau of Statistics (KNBS) and Society for International Development (SID) are grateful
to all the individuals directly involved in the publication of ‘Exploring Kenya’s Inequality: Pulling Apart or
Pulling Together?’ books. Special mention goes to Zachary Mwangi (KNBS, Ag. Director General) and
Ali Hersi (SID, Regional Director) for their institutional leadership; Katindi Sivi-Njonjo (SID, Progrmme
Director) and Paul Samoei (KNBS) for the effective management of the project; Eston Ngugi; Tabitha
Wambui Mwangi; Joshua Musyimi; Samuel Kipruto; George Kamula; Jason Lakin; Ali Zaidi; Leonard
Wanyama; and Irene Omari for the different roles played in the completion of these publications.
KNBS and SID would like to thank Bernadette Wanjala (KIPPRA), Mwende Mwendwa (KIPPRA), Raphael
Munavu (CRA), Moses Sichei (CRA), Calvin Muga (TISA), Chrispine Oduor (IEA), John T. Mukui, Awuor
Ponge (IPAR, Kenya), Othieno Nyanjom, Mary Muyonga (SID), Prof. John Oucho (AMADPOC), Ms. Ada
Mwangola (Vision 2030 Secretariat), Kilian Nyambu (NCIC), Charles Warria (DAP), Wanjiru Gikonyo
(TISA) and Martin Napisa (NTA), for attending the peer review meetings held on 3rd October 2012 and
Thursday, 28th Feb 2013 and for making invaluable comments that went into the initial production and
the finalisation of the books. Special mention goes to Arthur Muliro, Wambui Gathathi, Con Omore,
Andiwo Obondoh, Peter Gunja, Calleb Okoyo, Dennis Mutabazi, Leah Thuku, Jackson Kitololo, Yvonne
Omwodo and Maureen Bwisa for their institutional support and administrative assistance throughout the
project. The support of DANIDA through the Drivers of Accountability Project in Kenya is also gratefully
acknowledged.
Stefano PratoManaging Director,SID
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Striking Features on Intra-County Inequality in Kenya Inequalities within counties in all the variables are extreme. In many cases, Kenyans living within a
single county have completely different lifestyles and access to services.
Income/expenditure inequalities1. The five counties with the worst income inequality (measured as a ratio of the top to the bottom
decile) are in Coast. The ratio of expenditure by the wealthiest to the poorest is 20 to one and above
in Lamu, Tana River, Kwale, and Kilifi. This means that those in the top decile have 20 times as much
expenditure as those in the bottom decile. This is compared to an average for the whole country of
nine to one.
2. Another way to look at income inequality is to compare the mean expenditure per adult across
wards within a county. In 44 of the 47 counties, the mean expenditure in the poorest wards is less
than 40 percent the mean expenditure in the wealthiest wards within the county. In both Kilifi and
Kwale, the mean expenditure in the poorest wards (Garashi and Ndavaya, respectively) is less than
13 percent of expenditure in the wealthiest ward in the county.
3. Of the five poorest counties in terms of mean expenditure, four are in the North (Mandera, Wajir,
Turkana and Marsabit) and the last is in Coast (Tana River). However, of the five most unequal
counties, only one (Marsabit County) is in the North (looking at ratio of mean expenditure in richest
to poorest ward). The other four most unequal counties by this measure are: Kilifi, Kwale, Kajiado
and Kitui.
4. If we look at Gini coefficients for the whole county, the most unequal counties are also in Coast:
Tana River (.631), Kwale (.604), and Kilifi (.570).
5. The most equal counties by income measure (ratio of top decile to bottom) are: Narok, West Pokot,
Bomet, Nandi and Nairobi. Using the ratio of average income in top to bottom ward, the five most
equal counties are: Kirinyaga, Samburu, Siaya, Nyandarua, Narok.
Access to Education6. Major urban areas in Kenya have high education levels but very large disparities. Mombasa, Nairobi
and Kisumu all have gaps between highest and lowest wards of nearly 50 percentage points in
share of residents with secondary school education or higher levels.
7. In the 5 most rural counties (Baringo, Siaya, Pokot, Narok and Tharaka Nithi), education levels
are lower but the gap, while still large, is somewhat lower than that espoused in urban areas. On
average, the gap in these 5 counties between wards with highest share of residents with secondary
school or higher and those with the lowest share is about 26 percentage points.
8. The most extreme difference in secondary school education and above is in Kajiado County where
the top ward (Ongata Rongai) has nearly 59 percent of the population with secondary education
plus, while the bottom ward (Mosiro) has only 2 percent.
9. One way to think about inequality in education is to compare the number of people with no education
vii
Pulling Apart or Pooling Together?
to those with some education. A more unequal county is one that has large numbers of both. Isiolo
is the most unequal county in Kenya by this measure, with 51 percent of the population having
no education, and 49 percent with some. This is followed by West Pokot at 55 percent with no
education and 45 percent with some, and Tana River at 56 percent with no education and 44 with
some.
Access to Improved Sanitation10. Kajiado County has the highest gap between wards with access to improved sanitation. The best
performing ward (Ongata Rongai) has 89 percent of residents with access to improved sanitation
while the worst performing ward (Mosiro) has 2 percent of residents with access to improved
sanitation, a gap of nearly 87 percentage points.
11. There are 9 counties where the gap in access to improved sanitation between the best and worst
performing wards is over 80 percentage points. These are Baringo, Garissa, Kajiado, Kericho, Kilifi,
Machakos, Marsabit, Nyandarua and West Pokot.
Access to Improved Sources of Water 12. In all of the 47 counties, the highest gap in access to improved water sources between the county
with the best access to improved water sources and the least is over 45 percentage points. The
most severe gaps are in Mandera, Garissa, Marsabit, (over 99 percentage points), Kilifi (over 98
percentage points) and Wajir (over 97 percentage points).
Access to Improved Sources of Lighting13. The gaps within counties in access to electricity for lighting are also enormous. In most counties
(29 out of 47), the gap between the ward with the most access to electricity and the least access
is more than 40 percentage points. The most severe disparities between wards are in Mombasa
(95 percentage point gap between highest and lowest ward), Garissa (92 percentage points), and
Nakuru (89 percentage points).
Access to Improved Housing14. The highest extreme in this variable is found in Baringo County where all residents in Silale ward live
in grass huts while no one in Ravine ward in the same county lives in grass huts.
Overall ranking of the variables15. Overall, the counties with the most income inequalities as measured by the gini coefficient are Tana
River, Kwale, Kilifi, Lamu, Migori and Busia. However, the counties that are consistently mentioned
among the most deprived hence have the lowest access to essential services compared to others
across the following nine variables i.e. poverty, mean household expenditure, education, work for
pay, water, sanitation, cooking fuel, access to electricity and improved housing are Mandera (8
variables), Wajir (8 variables), Turkana (7 variables) and Marsabit (7 variables).
xi
Pulling Apart or Pooling Together?
Abbreviations
AMADPOC African Migration and Development Policy Centre
CRA Commission on Revenue Allocation
DANIDA Danish International Development Agency
DAP Drivers of Accountability Programme
EAs Enumeration Areas
HDI Human Development Index
IBP International Budget Partnership
IEA Institute of Economic Affairs
IPAR Institute of Policy Analysis and Research
KIHBS Kenya Intergraded Household Budget Survey
KIPPRA Kenya Institute for Public Policy Research and Analysis
KNBS Kenya National Bureau of Statistics
LPG Liquefied Petroleum Gas
NCIC National Cohesion and Integration Commission
NTA National Taxpayers Association
PCA Principal Component Analysis
SAEs Small Area Estimation
SID Society for International Development
TISA The Institute for Social Accountability
VIP latrine Ventilated-Improved Pit latrine
VOCs Volatile Organic Carbons
WDR World Development Report
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Exploring Kenya’s Inequality
A PUBLICATION OF KNBS AND SID
IntroductionBackgroundFor more than half a century many people in the development sector in Kenya have worked at alleviating
extreme poverty so that the poorest people can access basic goods and services for survival like food,
safe drinking water, sanitation, shelter and education. However when the current national averages are
disaggregated there are individuals and groups that still lag too behind. As a result, the gap between
the rich and the poor, urban and rural areas, among ethnic groups or between genders reveal huge
disparities between those who are well endowed and those who are deprived.
According to the world inequality statistics, Kenya was ranked 103 out of 169 countries making it the
66th most unequal country in the world. Kenya’s Inequality is rooted in its history, politics, economics
and social organization and manifests itself in the lack of access to services, resources, power, voice
and agency. Inequality continues to be driven by various factors such as: social norms, behaviours and
practices that fuel discrimination and obstruct access at the local level and/ or at the larger societal
level; the fact that services are not reaching those who are most in need of them due to intentional or
unintentional barriers; the governance, accountability, policy or legislative issues that do not favor equal
opportunities for the disadvantaged; and economic forces i.e. the unequal control of productive assets
by the different socio-economic groups.
According to the 2005 report on the World Social Situation, sustained poverty reduction cannot be
achieved unless equality of opportunity and access to basic services is ensured. Reducing inequality
must therefore be explicitly incorporated in policies and programmes aimed at poverty reduction. In
addition, specific interventions may be required, such as: affirmative action; targeted public investments
in underserved areas and sectors; access to resources that are not conditional; and a conscious effort
to ensure that policies and programmes implemented have to provide equitable opportunities for all.
This chapter presents the basic concepts on inequality and poverty, methods used for analysis,
justification and choice of variables on inequality. The analysis is based on the 2009 Kenya housing
and population census while the 2006 Kenya integrated household budget survey is combined with
census to estimate poverty and inequality measures from the national to the ward level. Tabulation of
both money metric measures of inequality such as mean expenditure and non-money metric measures
of inequality in important livelihood parameters like, employment, education, energy, housing, water
and sanitation are presented. These variables were selected from the census data and analyzed in
detail and form the core of the inequality reports. Other variables such as migration or health indicators
like mortality, fertility etc. are analyzed and presented in several monographs by Kenya National Bureau
of Statistics and were therefore left out of this report.
MethodologyGini-coefficient of inequalityThis is the most commonly used measure of inequality. The coefficient varies between ‘0’, which reflects
complete equality and ‘1’ which indicates complete inequality. Graphically, the Gini coefficient can be
3
Pulling Apart or Pooling Together?
easily represented by the area between the Lorenz curve and the line of equality. On the figure below,
the Lorenz curve maps the cumulative income share on the vertical axis against the distribution of the
population on the horizontal axis. The Gini coefficient is calculated as the area (A) divided by the sum
of areas (A and B) i.e. A/(A+B). If A=0 the Gini coefficient becomes 0 which means perfect equality,
whereas if B=0 the Gini coefficient becomes 1 which means complete inequality. Let xi be a point on
the X-axis, and yi a point on the Y-axis, the Gini coefficient formula is:
∑=
−− +−−=N
iiiii yyxxGini
111 ))((1 .
An Illustration of the Lorenz Curve
0
10
20
30
40
50
60
70
80
90
100
0 10 20 30 40 50 60 70 80 90 100
LORENZ CURVE
Cum
ulat
ive
% o
f Exp
endi
ture
Cumulative % of Population
A
B
Small Area Estimation (SAE)The small area problem essentially concerns obtaining reliable estimates of quantities of interest —
totals or means of study variables, for example — for geographical regions, when the regional sample
sizes are small in the survey data set. In the context of small area estimation, an area or domain
becomes small when its sample size is too small for direct estimation of adequate precision. If the
regional estimates are to be obtained by the traditional direct survey estimators, based only on the
sample data from the area of interest itself, small sample sizes lead to undesirably large standard errors
for them. For instance, due to their low precision the estimates might not satisfy the generally accepted
publishing criteria in official statistics. It may even happen that there are no sample members at all from
some areas, making the direct estimation impossible. All this gives rise to the need of special small area
estimation methodology.
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Exploring Kenya’s Inequality
A PUBLICATION OF KNBS AND SID
Most of KNBS surveys were designed to provide statistically reliable, design-based estimates only at
the national, provincial and district levels such as the Kenya Intergraded Household Budget Survey
of 2005/06 (KIHBS). The sheer practical difficulties and cost of implementing and conducting sample
surveys that would provide reliable estimates at levels finer than the district were generally prohibitive,
both in terms of the increased sample size required and in terms of the added burden on providers of
survey data (respondents). However through SAE and using the census and other survey datasets,
accurate small area poverty estimates for 2009 for all the counties are obtainable.
The sample in the 2005/06 KIHBS, which was a representative subset of the population, collected
detailed information regarding consumption expenditures. The survey gives poverty estimate of urban
and rural poverty at the national level, the provincial level and, albeit with less precision, at the district
level. However, the sample sizes of such household surveys preclude estimation of meaningful poverty
measures for smaller areas such as divisions, locations or wards. Data collected through censuses
are sufficiently large to provide representative measurements below the district level such as divisions,
locations and sub-locations. However, this data does not contain the detailed information on consumption
expenditures required to estimate poverty indicators. In small area estimation methodology, the first step
of the analysis involves exploring the relationship between a set of characteristics of households and
the welfare level of the same households, which has detailed information about household expenditure
and consumption. A regression equation is then estimated to explain daily per capita consumption
and expenditure of a household using a number of socio-economic variables such as household size,
education levels, housing characteristics and access to basic services.
While the census does not contain household expenditure data, it does contain these socio-economic
variables. Therefore, it will be possible to statistically impute household expenditures for the census
households by applying the socio-economic variables from the census data on the estimated
relationship based on the survey data. This will give estimates of the welfare level of all households
in the census, which in turn allows for estimation of the proportion of households that are poor and
other poverty measures for relatively small geographic areas. To determine how many people are
poor in each area, the study would then utilize the 2005/06 monetary poverty lines for rural and urban
households respectively. In terms of actual process, the following steps were undertaken:
Cluster Matching: Matching of the KIHBS clusters, which were created using the 1999 Population and
Housing Census Enumeration Areas (EA) to 2009 Population and Housing Census EAs. The purpose
was to trace the KIBHS 2005/06 clusters to the 2009 Enumeration Areas.
Zero Stage: The first step of the analysis involved finding out comparable variables from the survey
(Kenya Integrated Household Budget 2005/06) and the census (Kenya 2009 Population and Housing
Census). This required the use of the survey and census questionnaires as well as their manuals.
First Stage (Consumption Model): This stage involved the use of regression analysis to explore the
relationship between an agreed set of characteristics in the household and the consumption levels of
the same households from the survey data. The regression equation was then used to estimate and
explain daily per capita consumption and expenditure of households using socio-economic variables
5
Pulling Apart or Pooling Together?
such as household size, education levels, housing characteristics and access to basic services, and
other auxiliary variables. While the census did not contain household expenditure data, it did contain
these socio-economic variables.
Second Stage (Simulation): Analysis at this stage involved statistical imputation of household
expenditures for the census households, by applying the socio-economic variables from the census
data on the estimated relationship based on the survey data.
Identification of poor households Principal Component Analysis (PCA)In order to attain the objective of the poverty targeting in this study, the household needed to be
established. There are three principal indicators of welfare; household income; household consumption
expenditures; and household wealth. Household income is the theoretical indicator of choice of welfare/
economic status. However, it is extremely difficult to measure accurately due to the fact that many
people do not remember all the sources of their income or better still would not want to divulge this
information. Measuring consumption expenditures has many drawbacks such as the fact that household
consumption expenditures typically are obtained from recall method usually for a period of not more
than four weeks. In all cases a well planned and large scale survey is needed, which is time consuming
and costly to collect. The estimation of wealth is a difficult concept due to both the quantitative as well
as the qualitative aspects of it. It can also be difficult to compute especially when wealth is looked at as
both tangible and intangible.
Given that the three main indicators of welfare cannot be determined in a shorter time, an alternative
method that is quick is needed. The alternative approach then in measuring welfare is generally through
the asset index. In measuring the asset index, multivariate statistical procedures such the factor analysis,
discriminate analysis, cluster analysis or the principal component analysis methods are used. Principal
components analysis transforms the original set of variables into a smaller set of linear combinations
that account for most of the variance in the original set. The purpose of PCA is to determine factors (i.e.,
principal components) in order to explain as much of the total variation in the data as possible.
In this project the principal component analysis was utilized in order to generate the asset (wealth)
index for each household in the study area. The PCA can be used as an exploratory tool to investigate
patterns in the data; in identify natural groupings of the population for further analysis and; to reduce
several dimensionalities in the number of known dimensions. In generating this index information from
the datasets such as the tenure status of main dwelling units; roof, wall, and floor materials of main
dwelling; main source of water; means of human waste disposal; cooking and lighting fuels; household
items such radio TV, fridge etc was required. The recent available dataset that contains this information
for the project area is the Kenya Population and Housing Census 2009.
There are four main approaches to handling multivariate data for the construction of the asset index
in surveys and censuses. The first three may be regarded as exploratory techniques leading to index
construction. These are graphical procedures and summary measures. The two popular multivariate
procedures - cluster analysis and principal component analysis (PCA) - are two of the key procedures
that have a useful preliminary role to play in index construction and lastly regression modeling approach.
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Exploring Kenya’s Inequality
A PUBLICATION OF KNBS AND SID
In the recent past there has been an increasing routine application of PCA to asset data in creating
welfare indices (Gwatkin et al. 2000, Filmer and Pritchett 2001 and McKenzie 2003).
Concepts and definitionsInequalityInequality is characterized by the existence of unequal opportunities or life chances and unequal
conditions such as incomes, goods and services. Inequality, usually structured and recurrent, results
into an unfair or unjust gap between individuals, groups or households relative to others within a
population. There are several methods of measuring inequality. In this study, we consider among
other methods, the Gini-coefficient, the difference in expenditure shares and access to important basic
services.
Equality and EquityAlthough the two terms are sometimes used interchangeably, they are different concepts. Equality
requires all to have same/ equal resources, while equity requires all to have the same opportunity to
access same resources, survive, develop, and reach their full potential, without discrimination, bias, or
favoritism. Equity also accepts differences that are earned fairly.
PovertyThe poverty line is a threshold below which people are deemed poor. Statistics summarizing the bottom
of the consumption distribution (i.e. those that fall below the poverty line) are therefore provided. In
2005/06, the poverty line was estimated at Ksh1,562 and Ksh2,913 per adult equivalent1 per month
for rural and urban households respectively. Nationally, 45.2 percent of the population lives below the
poverty line (2009 estimates) down from 46 percent in 2005/06.
Spatial DimensionsThe reason poverty can be considered a spatial issue is two-fold. People of a similar socio-economic
background tend to live in the same areas because the amount of money a person makes usually, but
not always, influences their decision as to where to purchase or rent a home. At the same time, the area
in which a person is born or lives can determine the level of access to opportunities like education and
employment because income and education can influence settlement patterns and also be influenced
by settlement patterns. They can therefore be considered causes and effects of spatial inequality and
poverty.
EmploymentAccess to jobs is essential for overcoming inequality and reducing poverty. People who cannot access
productive work are unable to generate an income sufficient to cover their basic needs and those of
their families, or to accumulate savings to protect their households from the vicissitudes of the economy. 1This is basically the idea that every person needs different levels of consumption because of their age, gender, height, weight, etc. and therefore we take this into account to create an adult equivalent based on the average needs of the different populations
7
Pulling Apart or Pooling Together?
The unemployed are therefore among the most vulnerable in society and are prone to poverty. Levels
and patterns of employment and wages are also significant in determining degrees of poverty and
inequality. Macroeconomic policy needs to emphasize the need for increasing regular good quality
‘work for pay’ that is covered by basic labour protection. The population and housing census 2009
included questions on labour and employment for the population aged 15-64.
The census, not being a labour survey, only had few categories of occupation which included work
for pay, family business, family agricultural holdings, intern/volunteer, retired/home maker, full time
student, incapacitated and no work. The tabulation was nested with education- for none, primary and
secondary level.
EducationEducation is typically seen as a means of improving people’s welfare. Studies indicate that inequality
declines as the average level of educational attainment increases, with secondary education producing
the greatest payoff, especially for women (Cornia and Court, 2001). There is considerable evidence
that even in settings where people are deprived of other essential services like sanitation or clean
water, children of educated mothers have much better prospects of survival than do the children of
uneducated mothers. Education is therefore typically viewed as a powerful factor in leveling the field of
opportunity as it provides individuals with the capacity to obtain a higher income and standard of living.
By learning to read and write and acquiring technical or professional skills, people increase their chances
of obtaining decent, better-paying jobs. Education however can also represent a medium through
which the worst forms of social stratification and segmentation are created. Inequalities in quality and
access to education often translate into differentials in employment, occupation, income, residence and
social class. These disparities are prevalent and tend to be determined by socio-economic and family
background. Because such disparities are typically transmitted from generation to generation, access
to educational and employment opportunities are to a certain degree inherited, with segments of the
population systematically suffering exclusion. The importance of equal access to a well-functioning
education system, particularly in relation to reducing inequalities, cannot be overemphasized.
WaterAccording to UNICEF (2008), over 1.1 billion people lack access to an improved water source and over
three million people, mostly children, die annually from water-related diseases. Water quality refers
to the basic and physical characteristics of water that determines its suitability for life or for human
uses. The quality of water has tremendous effects on human health both in the short term and in the
long term. As indicated in this report, slightly over half of Kenya’s population has access to improved
sources of water.
SanitationSanitation refers to the principles and practices relating to the collection, removal or disposal of human
excreta, household waste, water and refuse as they impact upon people and the environment. Decent
sanitation includes appropriate hygiene awareness and behavior as well as acceptable, affordable and
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Exploring Kenya’s Inequality
A PUBLICATION OF KNBS AND SID
sustainable sanitation services which is crucial for the health and wellbeing of people. Lack of access
to safe human waste disposal facilities leads to higher costs to the community through pollution of
rivers, ground water and higher incidence of air and water borne diseases. Other costs include reduced
incomes as a result of disease and lower educational outcomes.
Nationally, 61 percent of the population has access to improved methods of waste disposal. A sizeable
population i.e. 39 percent of the population is disadvantaged. Investments made in the provision of
safe water supplies need to be commensurate with investments in safe waste disposal and hygiene
promotion to have significant impact.
Housing Conditions (Roof, Wall and Floor)Housing conditions are an indicator of the degree to which people live in humane conditions. Materials
used in the construction of the floor, roof and wall materials of a dwelling unit are also indicative of the
extent to which they protect occupants from the elements and other environmental hazards. Housing
conditions have implications for provision of other services such as connections to water supply,
electricity, and waste disposal. They also determine the safety, health and well being of the occupants.
Low provision of these essential services leads to higher incidence of diseases, fewer opportunities
for business services and lack of a conducive environment for learning. It is important to note that
availability of materials, costs, weather and cultural conditions have a major influence on the type of
materials used.
Energy fuel for cooking and lightingLack of access to clean sources of energy is a major impediment to development through health related
complications such as increased respiratory infections and air pollution. The type of cooking fuel or
lighting fuel used by households is related to the socio-economic status of households. High level
energy sources are cleaner but cost more and are used by households with higher levels of income
compared with primitive sources of fuel like firewood which are mainly used by households with a lower
socio-economic profile. Globally about 2.5 billion people rely on biomass such as fuel-wood, charcoal,
agricultural waste and animal dung to meet their energy needs for cooking.
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Exploring Kenya’s Inequality
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Isiolo County
Figure 9.1: Isiolo Population Pyramid
20 15 10 5 0 5 10 15 20
0-45-9
10-1415-19
20-2425-2930-3435-3940-4445-4950-5455-5960-64
65+
Female Male
Isiolo
PopulationIsiolo County has a child rich population, where 0-14 year olds constitute 45% of the total population. This is due to high fertility rates among women as shown by the highest percentage size of 4-6 members at 40%.
Employment The 2009 population and housing census covered in brief the labour status as tabulated below. The main variable of interest for inequality discussed in the text is work for pay by level of education. The other variables, notably family business, family agricultural holdings, intern/volunteer, retired/homemaker, fulltime student, incapacitated and no work are tabulated and presented in the annex table 9.3 up to ward level.
Table 9: Overall Employment by Education Levels in Isiolo County
Education LevelWork for pay
Family Business
Family Agricul-tural Holding
Intern/ Volunteer
Retired/ Home-maker
Fulltime Student Incapacitated No work
Number of Individuals
Total 16.6 16.3 33.0 1.0 12.0 10.7 0.5 10.0 71,260
None 7.3 17.2 50.4 0.8 15.8 0.2 0.7 7.5 34,723
Primary 16.8 16.9 22.4 1.1 10.7 19.0 0.3 12.8 20,740
Secondary+ 36.5 13.8 8.7 1.3 5.1 22.9 0.1 11.7 15,797
In Isiolo County, 7% of the residents with no formal education, 17% of those with a primary level of education and 37% of those with a secondary level of education or above are working for pay. Work for pay is highest in Nairobi at 49%; this is 12 percentage points above the level in Isiolo for those with a secondary level of education or above.
11
Pulling Apart or Pooling Together?
Gini Coefficient In this report, the Gini index measures the extent to which the distribution of consumption expenditure among individuals or households within an economy deviates from a perfectly equal distribution. A Gini index of ‘0’ rep-resents perfect equality, while an index of ‘1’ implies perfect inequality. Isiolo County’s Gini index is 0.431 com-pared with Turkana County, which has the least inequality nationally (0.283).
Figure 9.2: Isiolo County-Gini Coefficient by Ward
CHERAB
CHARI
SERICHO
KINNA
GARBATULLA
BURAT
OLDONYIRONGARE MARA
WABERABULA PESA
³0 25 5012.5 Kilometers
Location of IsioloCounty in Kenya
Isiolo County:Gini Coefficient by Ward
Legend
Gini Coefficient
0.60 - 0.72
0.48 - 0.59
0.36 - 0.47
0.24 - 0.35
0.11 - 0.23
County Boundary
12
Exploring Kenya’s Inequality
A PUBLICATION OF KNBS AND SID
EducationFigure 9.3: Isiolo County-Percentage of Population by Education attainment by Ward
CHERAB
CHARI
SERICHO
KINNAGARBATULLA
BURAT
OLDONYIRONGARE MARA
³
Location of IsioloCounty in Kenya
Percentage of Population by Education Attainment - Ward Level - Isiolo County
Legend
NonePrimary
County Boundary
Secondary and aboveWater Bodies
0 25 5012.5 Kilometers
Only 13% of Isiolo County residents have a secondary level of education or above. Isiolo North constituency has the highest share of residents with a secondary level of education or above at 16%. This is twice Isiolo South constituency, which has the lowest share of residents with a secondary level of education or above. Isiolo North constituency is 3 percentage points above the county average. Two wards, Wabera and Bula Pesa, have the high-est share of residents with a secondary level of education or above at 29% each. This is almost 10 times Oldonyiro ward, which has the lowest share of residents with a secondary level of education or above. Wabera and Bula Pesa are 16 percentage points above the county average.
A total of 36% of Isiolo County residents have a primary level of education only. Isiolo North constituency has the highest share of residents with a primary level of education only at 37%. This is 4 percentage points above Isiolo South constituency, which has the lowest share of residents with a primary level of education only. Isiolo North constituency is 1 percentage point above the county average. Bula Pesa ward has the highest share of residents with a primary level of education at 47%. This is three times Oldonyiro ward, which has the lowest share of resi-dents with primary only. Bula Pesa ward is 11 percentage points above the county average.
A total of 51% of Isiolo county residents have no formal education. Isiolo South constituency has the highest share of residents with no formal education at 60%.This is 13 percentage points above Isiolo North constituency, which has the lowest share of residents with no formal education. Isiolo South constituency is 9 percentage points above the county average. Oldonyiro ward has the highest percentage of residents with no formal education at 83%. This is almost four times Bula Pesa ward, which has the lowest percentage of residents with no formal edu-cation. Oldonyiro ward is 32 percentage points above the county average.
13
Pulling Apart or Pooling Together?
EnergyCooking Fuel
Figure 9.4: Percentage Distribution of Households by Source of Cooking Fuel in Isiolo County
Only 2% of residents in Isiolo County use liquefied petroleum gas (LPG), while 3% use paraffin, 65% use firewood and 29% use charcoal. Firewood is the most common cooking fuel by gender with 63% of male headed house-holds and 67% of female headed households using it.
Isiolo South constituency has the highest level of firewood use in Isiolo County at 94%.This is 40 percentage points above Isiolo North constituency, which has the lowest share at 54%. Isiolo South constituency is about 29 percentage points above the county average. Chari ward has the highest level of firewood use in Isiolo County at 98%.This is eight times Bula Pesa ward, which has the lowest share at 12%. Chari ward is 33 percentage points above the county average.
Isiolo North constituency has the highest level of charcoal use in Isiolo County at 39%.This is almost eight times Isiolo South constituency, which has the lowest share at 5%. Isiolo North constituency is about 10 percentage points above the county average. Bula Pesa ward has the highest level of charcoal use in Isiolo County at 74%.This is 72 percentage points more than Chari ward, which has the lowest share at 2%. Bula Pesa ward is 45 per-centage points above the county average.
LightingFigure 9.5: Percentage Distribution of Households by Source of Lighting Fuel in Isiolo County
A total of 19% of residents in Isiolo County use electricity as their main source of lighting. A further 31% use lan-terns, 20% use tin lamps, and 23% use fuel wood. Electricity use is more common in male headed households at 20% as compared with female headed households at 16%.
0.5 2.7 2.1 0.4
64.9
29.1
0.1 0.3 -
10.0 20.0 30.0 40.0 50.0 60.0 70.0
Electricity Paraffin LPG Biogas Firewood Charcoal Solar Other
Perc
enta
ge
Figure 9.4: Percentage Distribution of Households by Source of Cooking Fuel in Isiolo County
18.7
0.5
30.8
19.6
2.5
23.3
1.4 3.3
0.05.0
10.015.020.025.030.035.0
Electricity Pressure Lamp Lantern Tin Lamp Gas Lamp Fuelwood Solar Other
Perc
enta
ge
Figure 9.5:Percentage Distribution of Households by Source of Lighting Fuel in Isiolo County
14
Exploring Kenya’s Inequality
A PUBLICATION OF KNBS AND SID
60.6
0.7 0.3 1.5
20.8
5.9 1.8 3.4 5.1
0.010.020.030.040.050.060.070.0
Corrugated Iron Sheets
Tiles Concrete Asbestos sheets
Grass Makuti Tin Mud/Dung Other
Perc
enta
ge
Figure 9.7: Percentage Distribution of Households by Roof Material in Isiolo County
Isiolo North constituency has the highest level of electricity use at 26%. This is 25 percentage points above Isiolo South constituency, which has the lowest level of electricity use. Isiolo North is 7 percentage points above the county average. Wabera ward has the highest level of electricity use at 54%. That is 54 percentage points above Chari ward, which has the lowest level of electricity use. Wabera ward is 35 percentage points above the county average.
HousingFlooring
Figure 9.6: Percentage Distribution of Households by Floor Material in Isiolo County
In Isiolo County, 29% of residents have homes with cement floors, while 70% have earth floors. Less than 1% has wood and just 1% has tile floors. Isiolo North constituency has the highest share of cement floors at 38%. This is six times Isiolo South constituency, which has the lowest share of cement floors. Isiolo North constituency is 9 percentage points above the county average. Bula Pesa ward has the highest share of cement floors at 74%. This is 73 percentage points above Chari ward, which has the lowest share of cement floors. Bula Pesa ward is 45 percentage points above the county average.
Roofing
Figure 9.7: Percentage Distribution of Households by Roof Material in Isiolo County
In Isiolo County, less than 1% of residents have homes with concrete roofs, while 61% has corrugated iron roofs. Grass and makuti roofs cover 27% of homes, and 3% have mud/dung roofs.
Isiolo North constituency has the highest share of corrugated iron sheet roofs at 66%. This is 20 percentage points above Isiolo South constituency, which has the lowest share of corrugated iron sheet roofs. Isiolo North is 5 per-centage points above the county average. Bula Pesa ward has the highest share of corrugated iron sheet roofs at 96%. This is almost 11 times Oldonyiro ward, which has the lowest share of corrugated iron sheet roofs. Bula Pesa ward is 35 percentage points above the county average.
29.0
0.5 0.4
69.9
0.2 -
10.0 20.0 30.0 40.0 50.0 60.0 70.0 80.0
Cement Tiles Wood Earth Other
Perc
enta
ge
Figure 9.6: Percentage Distribution of Households by Floor Material in Isiolo County
15
Pulling Apart or Pooling Together?
Isiolo South constituency has the highest share of grass/makuti roofs at 52%. That is three times Isiolo North constituency, which has the lowest share of grass/makuti roofs. Isiolo South constituency is 25 percentage points above the county average. Garbatula ward has the highest share of grass/makuti roofs at 63%. This is 63 percent-age points above Bula Pesa ward, which has the lowest share. Garbatula ward is 36 percentage points above the county average.
Walls
Figure 9.8: Percentage Distribution of Households by Wall Material in Isiolo County
In Isiolo County, 17% of homes have either brick or stone walls, 35% have mud/wood or mud/cement walls, 22% have wood walls and 1% has corrugated iron walls. Another 18% have grass/thatched walls, while 7% have tin or other walls.
Isiolo North constituency has the highest share of brick/stone walls at 23%. That is seven times Isiolo South con-stituency, which has the lowest share of brick/stone walls. Isiolo North constituency is 6 percentage points above the county average. Bula Pesa ward has the highest share of brick/stone walls at 44%. This is 43 percentage points above Chari ward, which has the lowest share of brick/stone walls. Bula Pesa ward is 27 percentage points above the county average.
Isiolo South constituency has the highest share of mud with wood/cement walls at 47%. This is 17 percentage points above Isiolo North constituency, which has the lowest share of mud with wood/cement. Isiolo South con-stituency is12 percentage points above the county average. Sericho ward has the highest share of mud with wood/cement walls at 66%. That is almost 17 times Wabera ward, which has the lowest share of mud with wood/cement walls. Sericho ward is 31 percentage points above the county average.
10.36.7
31.1
3.7
22.3
1.2
17.7
4.92.1
0.05.0
10.015.020.025.030.035.0
Stone Brick/Block Mud/Wood Mud/Cement Wood only Coorugated Iron Sheets
Grass/Reeds Tin Other
Perc
enta
ge
Figure 9.8: Percentage Distribution of Households by Wall Material in Isiolo County
16
Exploring Kenya’s Inequality
A PUBLICATION OF KNBS AND SID
CHERAB
CHARI
SERICHO
KINNA
GARBATULLA
BURAT
OLDONYIRONGARE MARA
³
Percentage of Households with Improved and UnimprovedSource of Water - Ward Level - Isiolo County
0 25 5012.5 Kilometers
Location of IsioloCounty in Kenya
Legend
Unimproved Source of WaterImproved Source of waterWater Bodies
County Boundary
WaterImproved sources of water comprise protected spring, protected well, borehole, piped into dwelling, piped and rain water collection while unimproved sources include pond, dam, lake, stream/river, unprotected spring, unpro-tected well, jabia, water vendor and others.
In Isiolo County, 59% of residents use improved sources of water, with the rest relying on unimproved sources. There is no significant gender differential in use of improved sources with male headed households at 59% in comparison with 60% in female headed households.
Isiolo North constituency has the highest share of residents using improved sources of water at 60%.This is 2 per-centage points above Isiolo South constituency, which has the lowest share of residents using improved sources of water. Isiolo North constituency is 1 percentage point above the county average. Wabera ward has the highest share of residents using improved sources of water at 88% each. This is 18 times Oldonyiro ward, which has the lowest share using improved sources of water. Wabera ward is 29 percentage points above the county average.
Figure 9.9: Isiolo County-Percentage of Households with Improved and Unimproved Sources of Water by Ward
SanitationWhile 40% of residents in Isiolo county use improved sanitation, the rest use unimproved sanitation. There is no significant gender differential as 40% of male headed households and 41% of female headed households use improved sanitation.
Isiolo North constituency has the highest share of residents using improved sanitation at 43%. This is 9 percent-age points above Isiolo South constituency, which has the lowest share of residents using improved sanitation. Isiolo North constituency is 3 percentage points above the county average. Bula Pesa ward has the highest share of residents using improved sanitation at 81%. This is almost 41 times Oldonyiro ward, which has the lowest share of using improved sanitation. Bula Pesa ward is 41 percentage points above the county average.
17
Pulling Apart or Pooling Together?
Figure 9.10: Isiolo County –Percentage of Households with Improved and Unimproved Sanitation by Ward
Isiolo County Annex Tables
CHERAB
CHARI
SERICHO
KINNA
GARBATULLA
BURAT
OLDONYIRO NGARE MARA
WABERA
³
Percentage of Households with Improved and UnimprovedSanitation - Ward Level - Isiolo County
Legend
Improved SanitationUnimproved SanitationWater Bodies
County Boundary
Location of IsioloCounty in Kenya
0 25 5012.5 Kilometers
18
Exploring Kenya’s Inequality
A PUBLICATION OF KNBS AND SID
9. I
sio
loTa
ble 9
.1: G
ende
r, Age
gro
up, D
emog
raph
ic In
dica
tors
and
Hous
ehol
ds S
ize b
y Cou
nty C
onst
ituen
cy an
d W
ards
Coun
ty
Cons
titue
ncy
/War
ds
Gend
erAg
e gro
upDe
mog
raph
ic in
dica
tors
Porti
on o
f HH
Mem
bers
:
Tota
l Pop
Male
Fem
ale0-
5 yrs
0-14
yrs
10-1
8 yrs
15-3
4 yrs
15-6
4 yrs
65+ y
rsse
x Ra
tioTo
tal
depe
n-de
ncy
Ratio
Child
de-
pend
ency
Ra
tio
aged
de
pen-
denc
y
ratio
0-3
4-6
7+
tota
l
Keny
a37
,919,6
47
18,78
7,698
19
,131,9
49
7,035
,670
16,34
6,414
8,2
93,20
7 13
,329,7
17
20,24
9,800
1,3
23,43
3 0.9
82
0.8
73
0.8
07
0.0
65
41.5
38.4
20.1
8,493
,380
Rura
l26
,075,1
95
12,86
9,034
13
,206,1
61
5,059
,515
12,02
4,773
6,1
34,73
0 8,3
03,00
7 12
,984,7
88
1,065
,634
0.974
1.008
0.926
0.082
33
.241
.3 25
.4 5,2
39,87
9
Urba
n11
,844,4
52
5,918
,664
5,925
,788
1,976
,155
4,321
,641
2,158
,477
5,026
,710
7,265
,012
257,7
99
0.999
0.630
0.595
0.035
54
.833
.7 11
.5 3,2
53,50
1
Isiolo
Cou
nty
139,3
96
70,41
4
68
,982
27
,743
62,94
3
32,10
6
47
,020
71,26
0
5,
193
1.0
21
0.9
56
0.8
83
0.0
73
39.5
40
.3
20.2
3
0,877
Isi
olo N
orth
Co
nstitu
ency
96,69
9
47
,467
49,23
2
19,74
0
44
,110
21
,930
32,68
7
49
,154
3,435
0.964
0.967
0.897
0.070
42
.1
39.2
18
.6 22
149
Wab
era
16,26
4
7,9
08
8,356
2,9
57
6,891
3,8
70
6,009
8,8
75
49
8
0.946
0.833
0.776
0.056
43
.5
35.2
21
.3 36
47
BulaP
esa
22,20
3
10
,759
11,44
4
3,8
41
8,569
4,4
69
8,777
12
,981
65
3
0.940
0.710
0.660
0.050
55
.5
33.1
11
.3 61
45
Char
i
4,7
73
2,410
2,3
63
1,090
2,3
53
1,081
1,3
31
2,162
258
1.0
20
1.2
08
1.0
88
0.1
19
34.7
44
.9
20.4
1024
Cher
ab
15
,475
7,707
7,7
68
3,183
7,4
23
3,906
4,7
65
7,405
647
0.9
92
1.0
90
1.0
02
0.0
87
33.1
44
.4
22.5
3256
Ngar
e Mar
a
4,8
62
2,493
2,3
69
1,099
2,3
63
1,021
1,4
87
2,333
166
1.0
52
1.0
84
1.0
13
0.0
71
48.5
34
.4
17.1
1133
Bura
t
17
,916
8,774
9,1
42
4,045
8,7
46
4,197
5,6
42
8,450
720
0.9
60
1.1
20
1.0
35
0.0
85
35.7
42
.9
21.5
3822
Oldo
nyiro
15,20
6
7,4
16
7,790
3,5
25
7,765
3,3
86
4,676
6,9
48
49
3
0.952
1.189
1
.118
0.0
71
31.6
46
.0
22.4
3122
Isiolo
Sou
th
Cons
tituen
cy
42
,697
22,94
7
19
,750
8,003
18
,833
10
,176
14,33
3
22
,106
1,758
1.162
0.931
0.852
0.080
32
.9
43.1
24
.0 87
28
Garb
atulla
16,12
0
8,4
21
7,699
3,1
54
7,459
3,8
17
5,222
8,0
51
61
0
1.094
1.002
0.926
0.076
35
.6
44.1
20
.3 34
73
Kinn
a
14
,551
8,078
6,4
73
2,612
5,9
87
3,254
4,9
89
7,883
681
1.2
48
0.8
46
0.7
59
0.0
86
31.3
43
.0
25.7
2908
Seric
ho
12
,026
6,448
5,5
78
2,237
5,3
87
3,105
4,1
22
6,172
467
1.1
56
0.9
48
0.8
73
0.0
76
30.7
41
.8
27.5
2347
19
Pulling Apart or Pooling Together?
Table 9.2: Employment by County, Constituency and Wards
County/Constituency
/Wards
Work for pay
Family Busi-ness
Family Agricultural
Holding
Intern/Volun-
teer
Retired/Homemaker
Fulltime Student
Incapacitated No work No. of Individ-uals
Kenya 23.7 13.1 32.0 1.1 9.2 12.8 0.5 7.7 20,249,800
Rural 15.6 11.2 43.5 1.0 8.8 13.0 0.5 6.3 12,984,788
Urban 38.1 16.4 11.4 1.3 9.9 12.2 0.3 10.2 7,265,012 Isiolo County 16.6 16.3 33.0 1.0 12.0 10.7 0.5 10.0 71,260
Isiolo North Constituency
19.9
13.7 29.9
1.1 13.2 11.7 0.4
10.0 49,154
Wabera
30.9
12.2 11.4
1.9 11.0 18.1 0.5
14.1 8,875
BulaPesa
30.3
21.5 10.3
0.9 8.3 12.9 0.4
15.4 12,981
Chari
6.2
17.3 24.2
1.5 31.5 14.2 0.8
4.4 2,162
Cherab
9.0
15.5 35.4
0.7 16.3 13.4 0.6
9.1 7,405
Ngare Mara
23.9
7.1 50.9
1.2 9.1 4.8 0.2
2.9 2,333
Burat
16.4
12.0 38.2
1.6 11.9 10.9 0.4
8.7 8,450
Oldonyiro
5.1
2.0 69.3
0.3 19.4 2.3 0.1
1.5 6,948
Isiolo South Constituency
9.2
22.3 39.8
0.8 9.1 8.4 0.5
9.8 22,106
Garbatulla
7.4
20.6 37.4
1.2 16.0 6.5 0.3
10.5 8,051
Kinna
13.8
29.7 33.6
0.4 6.7 7.6 0.7
7.5 7,883
Sericho
5.8
15.2 50.8
0.8 3.3 11.9 0.7
11.6 6,172
Table 9.3: Employment and Education Levels by County, Constituency and Wards
County / constituency/
Wards
Education Total level
Work for pay
Family Business
Family Agri-
cultural Holding
Intern/ Volunteer
Retired/
Home-maker
Fulltime Student
Incapaci-tated
No work
No. of Indi-viduals
Kenya Total 23.7 13.1 32.0 1.1 9.2 12.8 0.5 7.7
20,249,800
Kenya None 11.1 14.0 44.4 1.7 14.7 0.8 1.2 12.1
3,154,356
Kenya Primary 20.7 12.6 37.3 0.8 9.6 12.1 0.4 6.5
9,528,270
Kenya Secondary+ 32.7 13.3 20.2 1.2 6.6 18.6 0.2 7.3
7,567,174
Rural Total 15.6 11.2 43.5 1.0 8.8 13.0 0.5 6.3
12,984,788
Rural None 8.5 13.6 50.0 1.4 13.9 0.7 1.2 10.7
2,614,951
Rural Primary 15.5 10.8 45.9 0.8 8.4 13.2 0.5 5.0
6,785,745
Rural Secondary+ 21.0 10.1 34.3 1.0 5.9 21.9 0.3 5.5
3,584,092
Urban Total 38.1 16.4 11.4 1.3 9.9 12.2 0.3 10.2
7,265,012
Urban None 23.5 15.8 17.1 3.1 18.7 1.5 1.6 18.8
539,405
20
Exploring Kenya’s Inequality
A PUBLICATION OF KNBS AND SID
Urban Primary 33.6 16.9 16.0 1.0 12.3 9.5 0.4 10.2
2,742,525
Urban Secondary+ 43.2 16.1 7.5 1.3 7.1 15.6 0.2 9.0
3,983,082
Isiolo Total 16.6 16.3 33.0 1.0 12.0 10.7 0.5 10.0
71,260
Isiolo None 7.3 17.2 50.4 0.8 15.8 0.2 0.7 7.5
34,723
Isiolo Primary 16.8 16.9 22.4 1.1 10.7 19.0 0.3 12.8
20,740
Isiolo Secondary+ 36.5 13.8 8.7 1.3 5.1 22.9 0.1 11.7
15,797 Isiolo North Constit-uency Total 19.9 13.7 29.9 1.1 13.2 11.7
0.4
10.0
49,154
Isiolo North Constit-uency None 8.0 11.7 51.3 1.1 19.5 0.3
0.7
7.4
20,955
Isiolo North Constit-uency Primary 19.9 16.2 19.8 1.1 11.4 18.8
0.3
12.5
14,891
Isiolo North Constit-uency Secondary+ 38.5 13.9 7.7 1.2 5.4 21.9
0.1
11.4
13,308
Wabera Wards Total 30.9 12.2 11.4 1.9 11.0 18.1
0.5
14.1
8,875
Wabera Wards None 17.6 11.2 19.4 2.9 22.3 0.9
1.6
24.1
1,740
Wabera Wards Primary 26.0 13.9 13.7 1.8 11.5 19.0
0.4
13.7
2,966
Wabera Wards Secondary+ 39.9 11.3 6.4 1.4 5.9 24.7
0.1
10.2
4,169
BulaPesa Wards Total 30.3 21.5 10.3 0.9 8.3 12.9
0.4
15.4
12,981
BulaPesa Wards None 18.6 21.0 19.1 0.8 19.4 0.9
1.5
18.6
2,164
BulaPesa Wards Primary 25.9 23.6 11.8 0.9 7.8 12.6
0.4
17.0
5,091
BulaPesa Wards Secondary+ 38.6 19.8 5.5 0.9 4.5 17.7
0.1
12.9
5,726
Chari Wards Total 6.2 17.3 24.2 1.5 31.5 14.2
0.8
4.4
2,162
Chari Wards None 3.4 18.5 32.0 1.2 40.7 0.2
1.3
2.7
1,246
Chari Wards Primary 5.6 17.8 15.8 1.7 22.5 31.1
0.2
5.5
659
Chari Wards Secondary+ 21.0 10.1 7.8 2.7 10.5 38.5 -
9.3
257
Cherab Wards Total 9.0 15.5 35.4 0.7 16.3 13.4
0.6
9.1
7,405
Cherab Wards None 5.1 17.3 48.7 0.8 20.0 0.1
0.9
7.2
4,259
Cherab Wards Primary 8.4 15.1 22.6 0.7 13.9 29.4
0.4
9.7
2,173
Cherab Wards Secondary+ 27.5 8.7 6.2 0.4 5.2 35.9
0.1
15.9
973
Ngare Mara Wards Total 23.9 7.1 50.9 1.2 9.1 4.8
0.2
2.9
2,333
Ngare Mara Wards None 5.3 8.2 71.2 0.9 12.8 -
0.2
1.4
1,338
Ngare Mara Wards Primary 27.1 7.4 35.7 0.8 6.6 16.5
0.2
5.8
502
Ngare Mara Wards Secondary+ 71.2 3.7 11.2 2.2 1.6 5.9 -
4.3
493
Burat Wards Total 16.4 12.0 38.2 1.6 11.9 10.9
0.4
8.7
8,450
21
Pulling Apart or Pooling Together?
Burat Wards None 12.0 14.9 50.6 1.7 12.9 0.4
0.6
7.0
4,369
Burat Wards Primary 16.4 10.1 29.4 1.1 12.6 19.7
0.3
10.5
2,827
Burat Wards Secondary+ 31.4 6.5 14.8 2.0 6.9 27.8 -
10.6
1,254
Oldonyiro Wards Total 5.1 2.0 69.3 0.3 19.4 2.3
0.1
1.5
6,948
Oldonyiro Wards None 1.9 1.3 74.8 0.3 20.3 0.0
0.1
1.2
5,839
Oldonyiro Wards Primary 9.2 3.7 49.0 0.5 18.0 16.3 -
3.3
673
Oldonyiro Wards Secondary+ 40.6 8.0 27.1 0.5 9.4 11.0 -
3.4
436 Isiolo South Constit-uency Total 9.2 22.3 39.8 0.8 9.1 8.4
0.5
9.8
22,106
Isiolo South Constit-uency None 6.3 25.6 49.0 0.5 10.3 0.2
0.6
7.5
13,768
Isiolo South Constit-uency Primary 8.9 18.6 29.2 1.2 8.9 19.5
0.4
13.4
5,849
Isiolo South Constit-uency Secondary+ 26.2 13.1 13.9 1.5 3.5 27.8
0.5
13.5
2,489
Garbatulla Wards Total 7.4 20.6 37.4 1.2 16.0 6.5
0.3
10.5
8,051
Garbatulla Wards None 3.6 22.6 47.1 0.7 17.7 0.2
0.4
7.7
5,473
Garbatulla Wards Primary 8.9 18.6 20.1 2.1 15.2 17.3
0.3
17.5
1,800
Garbatulla Wards Secondary+ 30.7 11.4 9.3 2.7 5.8 25.5
0.1
14.5
778
Kinna Wards Total 13.8 29.7 33.6 0.4 6.7 7.6
0.7
7.5
7,883
Kinna Wards None 12.1 36.7 38.1 0.3 6.6 0.2
0.9
5.1
4,612
Kinna Wards Primary 11.5 21.5 32.6 0.6 8.3 14.7
0.5
10.2
2,299
Kinna Wards Secondary+ 26.9 15.5 14.8 0.5 3.4 26.1
0.3
12.5
972
Sericho Wards Total 5.8 15.2 50.8 0.8 3.3 11.9
0.7
11.6
6,172
Sericho Wards None 2.9 16.1 65.5 0.5 3.8 0.1
0.8
10.4
3,683
Sericho Wards Primary 5.5 14.9 33.9 1.1 3.1 28.0
0.2
13.2
1,750
Sericho Wards Secondary+ 20.7 11.5 17.6 1.4 1.4 32.5
1.1
13.9
739
Table 9.4: Employment and Education Levels in Male Headed Household by County, Constituency and Wards
County /constituency Education Level
reached
Work for Pay
Family Business
Family Agricultural
holding
Internal/ Volun-
teer
Retired/Homemaker
Fulltime Student
Inca-paci-tated
No work
Population (15-64)
Kenya National Total 25.5 13.5 31.6 1.1 9.0 11.4 0.4 7.5 14,757,992
Kenya National None 11.4 14.3 44.2 1.6 13.9 0.9 1.0 12.6 2,183,284
Kenya National Primary 22.2 12.9 37.3 0.8 9.4 10.6 0.4 6.4 6,939,667
Kenya National Secondary+ 35.0 13.8 19.8 1.1 6.5 16.5 0.2 7.0 5,635,041
Rural Rural Total 16.8 11.6 43.9 1.0 8.3 11.7 0.5 6.3 9,262,744
Rural Rural None 8.6 14.1 49.8 1.4 13.0 0.8 1.0 11.4 1,823,487
Rural Rural Primary 16.5 11.2 46.7 0.8 8.0 11.6 0.4 4.9 4,862,291
22
Exploring Kenya’s Inequality
A PUBLICATION OF KNBS AND SID
Rural Rural Secondary+ 23.1 10.6 34.7 1.0 5.5 19.6 0.2 5.3 2,576,966
Urban Urban Total 40.2 16.6 10.9 1.3 10.1 10.9 0.3 9.7 5,495,248
Urban Urban None 25.8 15.5 16.1 3.0 18.2 1.4 1.3 18.7 359,797
Urban Urban Primary 35.6 16.9 15.4 1.0 12.8 8.1 0.3 9.9 2,077,376
Urban Urban Secondary+ 45.1 16.6 7.3 1.2 7.4 13.8 0.1 8.5 3,058,075
Isiolo Total 19.1 18.3 32.5 1.0 10.5
8.8
0.4
9.5 48,072
Isiolo None 8.2 19.8 49.9 0.8 13.2
0.2
0.5
7.4 23,335
Isiolo Primary 19.5 18.5 22.2 1.1 10.2
15.9
0.3
12.2 13,933
Isiolo Secondary+ 42.0 14.7 8.1 1.1 5.1
18.3
0.1
10.6 10,804
Isiolo North Constituency Total 23.7 15.1 28.2 1.1 11.8
9.9
0.3
9.8 31,662
Isiolo North Constituency None 9.4 13.2 50.5 1.1 17.2
0.2
0.6
7.8 12,723
Isiolo North Constituency Primary 23.2 17.9 19.2 1.1 10.9
15.4
0.3
12.0 9,842
Isiolo North Constituency Secondary+ 44.2 14.7 7.0 1.0 5.3
17.4
0.0
10.3 9,097
Wabera Ward Total 36.3 12.6 10.7 1.9 9.9
15.1
0.4
13.0 6,023
Wabera Ward None 21.7 9.4 17.7 3.5 20.7
0.7
1.7
24.7 1,075
Wabera Ward Primary 29.8 15.1 14.2 1.7 10.4
16.0
0.2
12.5 2,019
Wabera Ward Secondary+ 46.2 12.1 5.8 1.4 5.6
19.8
0.0
9.1 2,929
BulaPesa Ward Total 33.7 22.5 9.9 0.8 7.8
10.5
0.4
14.3 8,711
BulaPesa Ward None 22.4 22.3 18.0 0.8 16.2
0.8
1.4
18.2 1,283
BulaPesa Ward Primary 28.3 24.8 11.9 0.8 8.0
9.9
0.4
15.9 3,489
BulaPesa Ward Secondary+ 42.3 20.6 5.5 0.8 4.9
14.3
0.1
11.6 3,939
Chari Ward Total 6.1 19.6 27.4 1.4 28.9
11.2
0.6
4.8 1,451
Chari Ward None 2.6 20.9 35.3 1.2 36.0
0.1
0.9
3.0 861
Chari Ward Primary 5.9 20.5 18.6 1.4 21.7
25.0
0.2
6.6 424
Chari Ward Secondary+ 25.3 10.8 9.0 2.4 10.2
33.1
-
9.0 166
Cherab Ward Total 10.6 18.5 36.4 0.8 13.7
10.7
0.5
8.8 5,026
Cherab Ward None 5.9 20.3 49.4 0.8 15.9
0.1
0.6
7.1 2,990
Cherab Ward Primary 10.0 18.4 23.0 0.9 12.8
25.5
0.4
9.1 1,416
Cherab Ward Secondary+ 34.8 10.0 4.5 0.3 5.8
28.2
0.2
16.1 620
Ngare Mara Ward Total 34.2 6.7 45.6 1.0 6.2
4.0
-
2.3 1,443
Ngare Mara Ward None 7.1 7.9 73.3 1.3 9.0
-
-
1.3 745
Ngare Mara Ward Primary 40.9 8.6 26.5 0.3 4.8
13.7
-
5.1 313
23
Pulling Apart or Pooling Together?
Ngare Mara Ward Secondary+ 81.3 2.9 7.5 0.8 1.8
3.9
-
1.8 385
Burat Ward Total 19.1 12.1 38.1 1.5 11.0
8.9
0.2
9.0 5,299
Burat Ward None 13.0 14.2 51.5 1.6 11.5
0.3
0.4
7.5 2,738
Burat Ward Primary 20.0 11.1 28.6 1.4 12.3
16.0
0.1
10.5 1,788
Burat Ward Secondary+ 38.6 7.2 12.7 1.4 6.0
23.2
-
11.0 773
Oldonyiro Ward Total 6.8 2.6 68.2 0.2 18.5
1.9
0.1
1.7 3,709
Oldonyiro Ward None 2.4 1.7 74.7 0.2 19.5
-
0.1
1.3 3,031
Oldonyiro Ward Primary 11.7 4.6 47.8 0.5 18.6
13.2
-
3.6 393
Oldonyiro Ward Secondary+ 46.7 8.4 26.7 0.4 8.1
6.7
-
3.2 285
Isiolo South Constituency Total 10.2 24.5 40.6 0.8 7.9
6.7
0.4
8.9 16,410
Isiolo South Constituency None 6.8 27.7 49.2 0.5 8.4
0.1
0.5
6.9 10,612
Isiolo South Constituency Primary 10.7 20.1 29.5 1.2 8.6
16.9
0.3
12.6 4,091
Isiolo South Constituency Secondary+ 30.6 14.9 14.1 1.6 3.7
23.0
0.3
11.9 1,707
Garbatulla Ward Total 7.5 22.9 40.2 1.1 13.5
4.8
0.2
9.9 6,035
Garbatulla Ward None 3.5 24.8 49.7 0.6 14.0
0.1
0.2
7.1 4,240
Garbatulla Ward Primary 9.6 20.4 20.8 2.1 15.1
14.1
0.2
17.7 1,268
Garbatulla Ward Secondary+ 34.5 13.7 10.1 2.7 5.1
20.3
-
13.7 527
Kinna Ward Total 15.8 33.3 31.7 0.4 6.0
6.0
0.6
6.2 5,879
Kinna Ward None 13.5 40.9 34.4 0.3 5.6
0.1
0.7
4.5 3,581
Kinna Ward Primary 14.0 23.0 32.7 0.5 7.7
12.9
0.5
8.7 1,640
Kinna Ward Secondary+ 32.5 17.8 14.6 0.6 4.1
21.1
-
9.3 658
Sericho Ward Total 6.6 15.1 52.9 0.8 3.0
10.0
0.5
11.0 4,496
Sericho Ward None 3.0 15.3 67.4 0.5 3.3
0.1
0.6
9.8 2,791
Sericho Ward Primary 7.2 15.9 34.4 1.2 2.9
25.5
0.3
12.7 1,183
Sericho Ward Secondary+ 24.1 12.5 17.4 1.7 1.7
28.2
1.0
13.4 522
Table 9.5: Employment and Education Levels in Female Headed Households by County, Constituency and Wards
Education Level reached
Work for Pay
Family Business
Family Agricultural
holding
Internal/ Volunteer
Retired/Home-maker
Fulltime Student
Incapaci-tated
No work Population (15-64)
Kenya National Total 18.87 11.91 32.74 1.20 9.85 16.66 0.69 8.08 5,518,645
Kenya National None 10.34 13.04 44.55 1.90 16.45 0.80 1.76 11.17 974,824 Kenya National Primary 16.74 11.75 37.10 0.89 9.82 16.23 0.59 6.89 2,589,877
24
Exploring Kenya’s Inequality
A PUBLICATION OF KNBS AND SID
Kenya National Secondary+ 25.95 11.57 21.07 1.27 6.59 25.16 0.28 8.11 1,953,944
Rural Rural Total 31.53 15.66 12.80 1.54 9.33 16.99 0.54 11.60 1,781,078
Rural Rural None 8.36 12.26 50.31 1.60 15.77 0.59 1.67 9.44 794,993
Rural Rural Primary 13.02 9.90 43.79 0.81 9.49 17.03 0.60 5.36 1,924,111
Rural Rural Secondary+ 15.97 8.87 33.03 1.06 6.80 27.95 0.34 5.98 1,018,463
Urban Urban Total 12.83 10.12 42.24 1.04 10.09 16.51 0.76 6.40 3,737,567
Urban Urban None 19.09 16.50 19.04 3.22 19.45 1.70 2.18 18.83 179,831
Urban Urban Primary 27.49 17.07 17.79 1.13 10.76 13.93 0.55 11.29 665,766
Urban Urban Secondary+ 36.81 14.50 8.06 1.51 6.36 22.11 0.22 10.43 935,481
Isiolo Total 11.9% 12.2% 33.9% 1.1% 14.9% 14.5% .6% 10.8% 23335
Isiolo None 5.4% 11.9% 51.5% .9% 21.3% .4% 1.0% 7.6% 11386
Isiolo Primary 11.5% 13.3% 22.8% 1.1% 11.7% 25.2% .4% 13.9% 6817
Isiolo Secondary+ 26.7% 11.5% 9.6% 1.5% 5.1% 31.7% .2% 13.7% 5132
Isiolo North Constituency Total 13.7% 11.0% 32.7% 1.1% 15.7% 14.9% .6% 10.3% 17640
Isiolo North Constituency None 5.8% 9.4% 52.6% 1.0% 23.1% .4% .9% 6.8% 8230
Isiolo North Constituency Primary 13.9% 12.8% 20.8% 1.0% 12.5% 25.2% .4% 13.5% 5060
Isiolo North Constituency Secondary+ 28.4% 11.9% 8.9% 1.5% 5.4% 30.6% .1% 13.1% 4350
Wabera Ward Total 23.9% 10.6% 12.1% 1.7% 12.6% 23.0% .6% 15.5% 3021
Wabera Ward None 11.1% 14.0% 22.2% 2.0% 24.9% 1.2% 1.4% 23.3% 666
Wabera Ward Primary 20.2% 11.2% 12.3% 1.9% 13.4% 24.4% .7% 15.8% 975
Wabera Ward Secondary+ 32.7% 8.6% 7.1% 1.4% 6.0% 32.6% .1% 11.5% 1380
BulaPesa Ward Total 23.1% 19.5% 11.0% 1.0% 9.5% 17.7% .5% 17.8% 4247
BulaPesa Ward None 13.0% 19.1% 20.8% .9% 24.3% 1.0% 1.7% 19.1% 878
BulaPesa Ward Primary 20.4% 21.1% 11.7% .9% 7.6% 18.4% .4% 19.4% 1585
BulaPesa Ward Secondary+ 30.4% 18.2% 5.5% 1.1% 3.8% 25.2% .1% 15.6% 1784
Chari Ward Total 6.2% 12.5% 17.6% 1.8% 37.0% 20.3% 1.1% 3.5% 711
Chari Ward None 5.2% 13.2% 24.7% 1.3% 51.2% .3% 2.1% 2.1% 385
Chari Ward Primary 5.1% 12.8% 10.6% 2.1% 23.8% 42.1% 0.0% 3.4% 235
Chari Ward Secondary+ 13.2% 8.8% 5.5% 3.3% 11.0% 48.4% 0.0% 9.9% 91
Cherab Ward Total 5.5% 9.2% 33.4% .6% 21.5% 19.0% 1.0% 9.7% 2379
Cherab Ward None 3.2% 10.2% 47.0% .8% 29.7% .2% 1.6% 7.5% 1269
Cherab Ward Primary 5.3% 9.0% 21.9% .3% 15.9% 36.6% .4% 10.7% 757
Cherab Ward Secondary+ 14.7% 6.5% 9.1% .6% 4.2% 49.3% 0.0% 15.6% 353
Ngare Mara Ward Total 7.3% 7.6% 59.3% 1.5% 13.8% 6.1% .4% 4.0% 892
Ngare Mara Ward None 3.0% 8.6% 68.6% .3% 17.5% 0.0% .5% 1.3% 593
Ngare Mara Ward Primary 4.2% 5.3% 50.8% 1.6% 9.5% 21.2% .5% 6.9% 189
Ngare Mara Ward Secondary+ 35.5% 6.4% 23.6% 7.3% .9% 12.7% 0.0% 13.6% 110
Burat Ward Total 11.7% 11.9% 38.2% 1.7% 13.5% 14.2% .6% 8.2% 3151
Burat Ward None 10.4% 15.9% 49.0% 2.0% 15.1% .6% .9% 6.1% 1631
Burat Ward Primary 10.1% 8.5% 30.6% .6% 13.2% 26.0% .5% 10.6% 1039
Burat Ward Secondary+ 20.0% 5.4% 18.1% 2.9% 8.5% 35.1% 0.0% 10.0% 481
Oldonyiro Ward Total 3.1% 1.3% 70.6% .4% 20.4% 2.7% .1% 1.4% 3239
Oldonyiro Ward None 1.4% .9% 74.9% .4% 21.2% .0% .1% 1.1% 2808
Oldonyiro Ward Primary 5.7% 2.5% 50.7% .4% 17.1% 20.7% 0.0% 2.9% 280
Oldonyiro Ward Secondary+ 29.1% 7.3% 27.8% .7% 11.9% 19.2% 0.0% 4.0% 151
Isiolo South Constituency Total 6.3% 16.1% 37.5% .9% 12.6% 13.3% .9% 12.3% 5695
Isiolo South Constituency None 4.5% 18.4% 48.5% .7% 16.6% .4% 1.1% 9.6% 3156
Isiolo South Constituency Primary 4.8% 15.0% 28.3% 1.3% 9.6% 25.4% .4% 15.1% 1757
25
Pulling Apart or Pooling Together?
Isiolo South Constituency Secondary+ 16.8% 9.1% 13.6% 1.2% 3.2% 38.2% .9% 17.1% 782
Garbatulla Ward Total 7.0% 13.9% 29.3% 1.6% 23.7% 11.4% .6% 12.5% 2015
Garbatulla Ward None 3.8% 15.2% 38.4% 1.2% 30.5% .5% .7% 9.7% 1233
Garbatulla Ward Primary 7.0% 14.1% 18.5% 2.1% 15.6% 25.0% .6% 17.1% 531
Garbatulla Ward Secondary+ 22.7% 6.8% 7.6% 2.8% 7.2% 36.3% .4% 16.3% 251
Kinna Ward Total 7.9% 19.1% 39.3% .5% 8.5% 12.3% 1.0% 11.3% 2004
Kinna Ward None 7.3% 22.4% 51.1% .3% 9.7% .6% 1.5% 7.2% 1031
Kinna Ward Primary 5.5% 17.8% 32.3% .9% 9.9% 19.1% .5% 14.1% 659
Kinna Ward Secondary+ 15.0% 10.8% 15.3% .3% 1.9% 36.6% 1.0% 19.1% 314
Sericho Ward Total 3.6% 15.2% 45.2% .6% 4.2% 16.9% 1.0% 13.3% 1676
Sericho Ward None 2.4% 18.3% 59.6% .4% 5.5% .2% 1.3% 12.2% 892
Sericho Ward Primary 2.1% 12.7% 33.0% .9% 3.7% 33.2% .2% 14.3% 567
Sericho Ward Secondary+ 12.4% 9.2% 18.0% .5% .5% 42.9% 1.4% 15.2% 217
Table 9.6: Gini Coefficient by county Constituency and Ward
County/Constituency/Wards Pop. Share Mean Consump. Share Gini
Kenya 1 3,440 1 0.445
Rural 0.688 2,270 0.454 0.361
Urban 0.312 6,010 0.546 0.368
Isiolo County 0.005 3,030 0.004 0.431
Isiolo North Constituency 0.004 3,440 0.0039 0.421
Wabera 0.001 4,100 0.0010 0.371
BulaPesa 0.001 5,150 0.0018 0.385
Chari 0.000 1,420 0.0001 0.247
Cherab 0.001 1,980 0.0003 0.279
Ngare Mara 0.000 2,910 0.0001 0.423
Burat 0.001 2,480 0.0005 0.316
Oldonyiro 0.000 1,620 0.0002 0.299
Isiolo South Constituency 0.001 1,620 0.0005 0.311
Garbatulla 0.000 1,610 0.0002 0.340
Kinna 0.000 1,880 0.0002 0.286
Sericho 0.000 1,320 0.0001 0.254
Table 9.7: Education by County, Constituency and Wards
County/Constituency /Wards None Primary Secondary+ Total Pop
Kenya 25.2 52.0 22.8 34,024,396
Rural 29.5 54.7 15.9 23,314,262
Urban 15.8 46.2 38.0 10,710,134
Isiolo County 51.1 36.1 12.9 125,192
Isiolo North Constituency 47.1 37.3 15.7 86,578
Wabera 26.8 44.1 29.1 14,694
BulaPesa 24.3 46.8 29.0 20,127
Chari 52.9 40.9 6.2 4,244
Cherab 53.9 39.0 7.1 13,885
Ngare Mara 61.4 27.0 11.6 4,286
26
Exploring Kenya’s Inequality
A PUBLICATION OF KNBS AND SID
Burat 53.0 38.9 8.1 15,884
Oldonyiro 82.8 13.9 3.3 13,458
Isiolo South Constituency 60.0 33.4 6.6 38,614
Garbatulla 67.7 26.9 5.4 14,575
Kinna 55.7 36.8 7.5 13,187
Sericho 54.7 38.2 7.0 10,852
Table 9.8: Education for Male and Female Headed Households by County, Constituency and Ward
County/Constituency/Wards None Primary Second-ary+
Total Pop None Primary Secondary+ Total Pop
Male Female
Kenya 23.5 51.8 24.7 16,819,031 26.8 52.2 21.0 17,205,365
Rural 27.7 54.9 17.4 11,472,394 31.2 54.4 14.4 11,841,868
Urban 14.4 45.2 40.4 5,346,637 17.2 47.2 35.6 5,363,497
Isiolo County 48.2 36.7 15.1 62,968 53.9 35.5 10.6 62,224
Isiolo North Constituency 43.1 38.5 18.4 42,147 50.8 36.0 13.1 44,431
Wabera 23.4 43.8 32.8 7,108 30.0 44.4 25.6 7,586
BulaPesa 21.3 46.7 32.0 9,654 27.0 46.8 26.2 10,473
Chari 49.6 42.3 8.2 2,111 56.2 39.6 4.3 2,133
Cherab 49.9 40.6 9.5 6,849 57.8 37.5 4.7 7,036
Ngare Mara 53.4 28.4 18.2 2,192 69.9 25.5 4.6 2,094
Burat 48.9 40.9 10.2 7,727 56.9 37.1 6.1 8,157
Oldonyiro 77.5 17.9 4.6 6,506 87.8 10.2 2.0 6,952
Isiolo South Constituency 58.5 33.0 8.5 20,821 61.7 34.0 4.4 17,793
Garbatulla 65.9 26.8 7.3 7,617 69.8 26.9 3.4 6,958
Kinna 55.2 35.4 9.4 7,379 56.4 38.5 5.1 5,808
Sericho 53.2 38.0 8.9 5,825 56.6 38.5 4.9 5,027
Table 9.9: Cooking Fuel by County, Constituency and Wards
County/Constituency/Wards Electricity Paraffin LPG Biogas Firewood Charcoal Solar Other Households
Kenya 0.8 11.7 5.1 0.7 64.4
17.0 0.1 0.3 8,493,380
Rural 0.2 1.4 0.6 0.3 90.3
7.1 0.1 0.1 5,239,879
Urban 1.8 28.3 12.3 1.4 22.7
32.8 0.0 0.6 3,253,501
Isiolo County 0.5 2.7 2.1 0.4 64.9
29.1 0.1 0.3 30,877
Isiolo North Constituency 0.6 3.5 2.8
0.5 53.5
38.6
0.1 0.3 22,149
Wabera 1.5 6.1 5.8
0.7 14.9
70.6
- 0.4 3,647
BulaPesa 0.6 7.8 3.3
0.9 12.4
74.4
0.0 0.5 6,145
Chari - - 0.1
0.1 97.8
1.6
- 0.5 1,024
Cherab 0.0 0.2 0.2
0.2 96.3
2.9
0.0 0.2 3,256
Ngare Mara 2.4 1.5 8.7
0.4 72.6
14.2
0.1 0.1 1,133
27
Pulling Apart or Pooling Together?
Burat 0.4 1.3 2.4
0.2 70.2
25.1
0.2 0.3 3,822
Oldonyiro - 0.4 0.3
0.2 93.2
5.8
0.2 0.1 3,122
Isiolo South Constituency 0.0 0.5 0.3
0.1 93.9
4.9
0.1 0.2 8,728
Garbatulla 0.1 0.6 0.4
0.1 92.2
6.2
0.1 0.3 3,473
Kinna 0.1 0.5 0.1
0.1 95.5
3.5
0.2 0.1 2,908
Sericho - 0.2 0.3
0.1 94.5
4.7
0.0 0.2 2,347
Table 9.10: Cooking Fuel for Male Headed Households by County, Constituency and Wards
County/Constituency/Wards Electricity Paraffin LPG Biogas Firewood Charcoal Solar Other Households
Kenya 0.9 13.5 5.3 0.8 61.4 17.7 0.1 0.4 5,762,320
Rural 0.2 1.6 0.6 0.3 89.6 7.5 0.1 0.1 3,413,616
Urban 1.9 30.9 12.0 1.4 20.4 32.5 0.0 0.7 2,348,704
Isiolo County 0.6 3.4 2.5 0.4 63.4 29.3 0.1 0.3 19,020
Isiolo North Constituency 0.8 4.7 3.5 0.6 49.7 40.2 0.1 0.4 13,062
Wabera 1.8 8.4 6.4 0.8 14.3 67.8 0.0 0.5 2,333
BulaPesa 0.7 9.3 3.5 0.8 12.0 73.1 0.0 0.7 3,841
Chari 0.0 0.0 0.2 0.2 97.4 1.9 0.0 0.3 621
Cherab 0.1 0.3 0.3 0.4 95.3 3.6 0.0 0.2 1,951
Ngare Mara 3.6 2.1 12.3 0.7 60.6 20.4 0.1 0.1 673
Burat 0.4 1.7 3.4 0.3 68.9 24.7 0.2 0.3 2,206
Oldonyiro 0.0 0.7 0.4 0.3 91.1 7.3 0.1 0.1 1,437
Isiolo South Constituency 0.0 0.6 0.3 0.2 93.5 5.2 0.1 0.2 5,958
Garbatulla 0.0 0.9 0.4 0.2 91.8 6.2 0.2 0.4 2,397
Kinna 0.1 0.5 0.1 0.2 94.5 4.3 0.2 0.1 1,952
Sericho 0.0 0.2 0.2 0.1 94.6 4.7 0.0 0.1 1,609
Table 9.11: Cooking Fuel for Female Headed Households by County, Constituency and Wards
County/Constituency/Wards Electricity Paraffin LPG Biogas Firewood Charcoal Solar Other Households
Kenya 0.6 7.9 4.6 0.7 70.6 15.5 0.0 0.1 2,731,060
Rural 0.1 1.0 0.5 0.3 91.5 6.5 0.0 0.1 1,826,263
Urban 1.6 21.7 13.0 1.5 28.5 33.6 0.0 0.3 904,797
Isiolo County 0.3 1.5 1.5 0.3 67.4 28.8 0.1 0.2 11,857
Isiolo North Constituency 0.4 1.8 1.8 0.4 59.0 36.3 0.1 0.2 9,087
Wabera 0.8 2.1 4.6 0.7 16.1 75.6 - 0.2 1,314
BulaPesa 0.6 5.3 3.0 1.1 13.0 76.7 0.0 0.2 2,304
Chari - - - - 98.3 1.0 - 0.7 403
Cherab - - 0.2 - 97.7 1.8 0.1 0.2 1,305
Ngare Mara 0.7 0.7 3.5 - 90.0 5.2 - - 460
Burat 0.4 0.8 1.1 0.1 71.8 25.6 0.1 0.2 1,616
Oldonyiro - 0.1 0.1 0.1 95.1 4.5 0.2 0.1 1,685
Isiolo South Constituency 0.1 0.2 0.3 0.0 94.8 4.3 0.1 0.1 2,770
Garbatulla 0.2 0.1 0.5 0.1 92.9 6.0 - 0.2 1,076
28
Exploring Kenya’s Inequality
A PUBLICATION OF KNBS AND SID
Kinna - 0.4 - - 97.4 2.0 0.2 - 956
Sericho - 0.1 0.5 - 94.2 4.7 0.1 0.3 738
Table 9.12: Lighting Fuel by County, Constituency and Wards
County/Constituency/Wards Electricity Pressure Lamp Lantern Tin Lamp Gas Lamp Fuelwood Solar Other Households
Kenya 22.9 0.6 30.6 38.5 0.9 4.3 1.6 0.6
5,762,320
Rural 5.2 0.4 34.7 49.0 1.0 6.7 2.2 0.7
3,413,616
Urban 51.4 0.8 23.9 21.6 0.6 0.4 0.7 0.6
2,348,704
Isiolo County 18.7 0.5 30.8 19.6 2.5 23.3 1.4 3.3 19,020
Isiolo North Constituency 25.7 0.5 24.8 22.0 2.7 21.2 1.3 1.8 13,062
Wabera 53.8 0.7 24.0 18.9 0.3 0.5 1.8 0.1 2,333
BulaPesa 49.5 0.3 26.3 22.5 0.2 0.0 0.9 0.2 3,841
Chari 0.1 0.2 32.4 39.4 4.4 12.7 1.3 9.6 621
Cherab 4.6 0.0 42.4 19.4 8.3 15.4 2.0 7.9 1,951
Ngare Mara 24.0 0.1 10.2 23.8 0.1 41.0 0.8 0.0 673
Burat 7.1 1.4 26.1 35.0 6.4 22.4 1.3 0.4 2,206
Oldonyiro 0.2 0.0 5.5 5.0 0.6 87.5 0.9 0.3 1,437
Isiolo South Constituency 0.7 0.6 46.2 13.4 2.0 28.5 1.5 7.0 5,958
Garbatulla 0.8 1.1 33.3 13.7 0.5 37.6 2.1 10.8 2,397
Kinna 1.0 0.3 43.9 21.4 4.3 25.4 1.1 2.5 1,952
Sericho 0.1 0.2 68.3 3.1 1.5 18.9 1.2 6.8 1,609
Table 9.13: Lighting Fuel for Male Headed Households by County, Constituency and Wards
County/Constituency/Wards Electricity Pressure Lamp Lantern Tin Lamp Gas Lamp Fuelwood Solar Other Households
Kenya 24.6 0.6 30.4 36.8 0.9 4.2 1.7 0.7
5,762,320
Rural 5.6 0.5 35.3 47.5 1.1 6.8 2.4 0.7
3,413,616
Urban 52.4 0.9 23.3 21.2 0.6 0.4 0.7 0.7
2,348,704
Isiolo County 20.1 0.5 30.1 18.8 2.2 23.1 1.4 3.7 19,020
Isiolo North Constituency 29.0 0.5 24.6 22.0 2.4 18.0 1.3 2.2 13,062
Wabera 55.9 0.6 22.4 18.8 0.2 0.4 1.7 0.1 2,333
BulaPesa 51.0 0.4 24.9 22.2 0.2 0.0 0.9 0.3 3,841
29
Pulling Apart or Pooling Together?
Chari 0.2 0.2 27.7 39.1 4.8 16.4 1.1 10.5 621
Cherab 4.8 0.0 40.7 17.9 7.0 17.5 2.2 9.8 1,951
Ngare Mara 36.3 0.1 12.3 20.4 0.0 30.2 0.7 0.0 673
Burat 8.1 1.5 25.9 34.7 5.9 22.1 1.3 0.6 2,206
Oldonyiro 0.2 0.0 7.4 5.8 0.8 84.1 1.3 0.5 1,437
Isiolo South Constituency 0.8 0.6 42.2 11.8 1.7 34.4 1.5 7.0 5,958
Garbatulla 1.0 1.2 28.6 12.5 0.5 43.6 2.0 10.7 2,397
Kinna 1.0 0.3 40.8 18.5 3.6 32.4 1.0 2.4 1,952
Sericho 0.1 0.2 64.2 2.7 1.4 23.1 1.2 7.2 1,609
Table 9.14: Lighting Fuel for Female Headed Households by County, Constituency and Wards
County/Constituency/Wards Electricity Pressure Lamp
Lantern Tin Lamp Gas Lamp Fuelwood Solar Other Households
Kenya 19.2 0.5 31.0
42.1 0.8 4.5
1.4
0.5 2,731,060
Rural 4.5 0.4 33.7
51.8 0.8 6.5
1.8
0.5 1,826,263
Urban 48.8 0.8 25.4
22.6 0.7 0.6
0.6
0.5 904,797
Isiolo County 16.3 0.4 32.1 20.9 3.0 23.5
1.3
2.5 11,857
Isiolo North Constituency 21.1 0.4 25.1 22.1 3.1 25.8
1.2
1.2 9,087
Wabera 50.2 0.8 26.8 19.1 0.5 0.5
2.0
0.1 1,314
BulaPesa 46.9 0.2 28.6 23.1 0.2 -
0.9
0.1 2,304
Chari - 0.2 39.7 39.7 3.7 6.9
1.5
8.2 403
Cherab 4.3 0.1 44.9 21.6 10.1 12.3
1.7
5.1 1,305
Ngare Mara 6.1 - 7.2 28.9 0.2 56.7
0.9 - 460
Burat 5.8 1.2 26.4 35.5 7.1 22.8
1.2
0.1 1,616
Oldonyiro 0.1 - 3.9 4.3 0.4 90.4
0.7
0.2 1,685
Isiolo South Constituency 0.6 0.5 54.9 16.9 2.7 16.0
1.6
6.8 2,770
Garbatulla 0.4 1.0 43.8 16.5 0.6 24.4
2.2
11.1 1,076
Kinna 1.2 0.2 50.2 27.2 5.9 11.3
1.4
2.7 956
Sericho 0.1 0.3 77.1 4.1 1.8 9.8
1.1
5.8 738
30
Exploring Kenya’s Inequality
A PUBLICATION OF KNBS AND SID
Table 9.15: Main material of the Floor by County, Constituency and Wards
County/Constituency/ wards Cement Tiles Wood Earth Other Households
Kenya 41.2 1.6 0.7 56.0 0.5 8,493,380
Rural 22.1 0.3 0.7 76.5 0.4 5,239,879
Urban 71.8 3.5 0.9 23.0 0.8 3,253,501
Isiolo County 29.0 0.5 0.4 69.9 0.2 30,877
Isiolo North Constituency 37.9 0.7 0.4 60.7 0.2 22,149
Wabera 67.6 2.6 0.5 29.2 0.1 3,647
BulaPesa 73.8 0.7 0.7 24.8 0.1 6,145
Chari 1.3 - 0.6 97.8 0.4 1,024
Cherab 3.9 0.0 0.2 95.2 0.6 3,256
Ngare Mara 29.7 0.4 0.1 69.5 0.4 1,133
Burat 20.4 0.3 0.6 78.6 0.1 3,822
Oldonyiro 4.5 0.3 0.1 95.1 0.1 3,122
Isiolo South Constituency 6.3 0.1 0.3 93.0 0.3 8,728
Garbatulla 7.2 0.2 0.3 92.1 0.2 3,473
Kinna 7.1 0.0 0.2 92.1 0.6 2,908
Sericho 3.8 0.1 0.3 95.5 0.2 2,347
Table 9.16: Main Material of the Floor in Male and Female Headed Households by County, Constituency and Ward
County/Constituen-cy/ wards
Cement Tiles Wood Earth Other Households Cement Tiles Wood Earth Other House-holds
Male Female
Kenya 42.8 1.6 0.8 54.2 0.6 5,762,320 37.7 1.4 0.7 59.8 0.5 2,731,060
Rural 22.1 0.3 0.7 76.4 0.4 3,413,616 22.2 0.3 0.6 76.6 0.3 1,826,263
Urban 72.9 3.5 0.9 21.9 0.8 2,348,704 69.0 3.6 0.9 25.8 0.8 904,797
Isiolo County
30.6
0.6
0.4
68.1
0.3 19,020 26.3
0.5
0.4
72.7
0.1 11,857 Isiolo North Constit-uency
41.8
0.8
0.5
56.7
0.3 13,062 32.3
0.6
0.4
66.6
0.1 9,087
Wabera
68.8
2.6
0.5
28.1
0.1 2,333 65.5
2.7
0.5
31.1
0.2 1,314
BulaPesa
75.4
0.8
0.7
23.1
0.1 3,841 71.1
0.5
0.7
27.7
0.1 2,304
Chari
1.3 -
0.8
97.3
0.6 621 1.2
-
0.2
98.5
- 403
Cherab
4.2
0.1
0.3
94.5
1.0 1,951 3.4
-
0.1
96.4
0.1 1,305
Ngare Mara
44.0
0.4 -
54.8
0.7 673 8.7
0.2
0.2
90.9
- 460
Burat
21.5
0.3
0.5
77.6
0.0 2,206 18.9
0.2
0.7
80.1
0.1 1,616
Oldonyiro
6.6
0.3
0.1
92.8
0.1 1,437 2.7
0.2
0.2
97.0
- 1,685 Isiolo South Constit-uency
6.2
0.1
0.3
93.1
0.3 5,958 6.5
0.2
0.2
92.8
0.3 2,770
Garbatulla
7.0
0.1
0.3
92.4
0.2 2,397 7.8
0.4
0.4
91.4
0.1 1,076
Kinna
6.8 -
0.3
92.4
0.5 1,952 7.6
0.1
0.1
91.4
0.7 956
Sericho
4.2
0.1
0.4
95.0
0.3 1,609 3.0
0.1
0.1
96.7
- 738
31
Pulling Apart or Pooling Together?
Table 9.17: Main Roofing Material by County Constituency and Wards
County/Constituen-cy/Wards
Corrugated Iron Sheets
Tiles Concrete Asbestos sheets
Grass Makuti Tin Mud/Dung Other House-holds
Kenya 73.5 2.2 3.6 2.2 13.3 3.2 0.3 0.8 1.0
8,493,380
Rural 70.3 0.7 0.2 1.8 20.2 4.2 0.2 1.2 1.1
5,239,879
Urban 78.5 4.6 9.1 2.9 2.1 1.5 0.3 0.1 0.9
3,253,501
Isiolo County 60.6 0.7 0.3 1.5 20.8 5.9 1.8 3.4 5.1 30,877
Isiolo North Constit-uency 66.3 0.6 0.4 2.0 12.4 4.3 2.5 4.7 6.8
22,149
Wabera 92.4 2.5 1.6 2.6 0.7 0.0 0.1 0.0 0.1 3,647
BulaPesa 96.4 0.2 0.6 2.5 0.1 0.1 0.0 0.0 0.0 6,145
Chari 60.5 0.2 0.0 0.1 22.1 14.9 0.0 0.0 2.1 1,024
Cherab 55.3 0.2 0.0 0.3 25.4 12.3 0.0 0.0 6.4 3,256
Ngare Mara 28.0 0.4 0.1 8.6 41.0 1.5 0.2 8.6 11.6 1,133
Burat 62.3 0.5 0.0 1.8 25.0 1.5 1.3 3.4 4.2 3,822
Oldonyiro 8.6 0.0 0.1 0.6 7.5 10.3 15.7 25.9 31.3 3,122
Isiolo South Constit-uency 46.2 0.8 0.0 0.1 42.0 9.8 0.1 0.1 1.0
8,728
Garbatulla 33.9 1.4 0.0 0.0 51.4 11.9 0.1 0.1 1.2 3,473
Kinna 48.0 0.1 0.0 0.1 43.9 7.4 0.1 0.1 0.3 2,908
Sericho 61.9 0.8 0.0 0.3 25.9 9.6 0.0 0.0 1.4 2,347
Table 9.18: Main Roofing Material in Male Headed Households by County, Constituency and Wards
County/Constituency/ WardsCorrugat-ed Iron Sheets
Tiles Concrete Asbestos sheets
Grass Makuti Tin Mud/Dung
Other Households
Kenya73.0
2.3 3.9
2.3
13.5
3.2
0.3
0.5
1.0 5,762,320
Rural69.2
0.8 0.2
1.8
21.5
4.4
0.2
0.9
1.1 3,413,616
Urban78.5
4.6 9.3
2.9
2.0
1.4
0.3
0.1
0.9 2,348,704
Isiolo County 60.4
0.8
0.3
1.7
22.2
5.9
1.4
2.5
4.7 19,020
Isiolo North Constituency 69.0
0.7
0.5
2.4
11.5
4.0
2.0
3.6
6.2 13,062
Wabera 92.8
2.6
1.7
2.3
0.4
0.0
0.0
-
0.1 2,333
BulaPesa 96.1
0.2
0.7
2.8
0.1
0.1
0.0
-
0.0 3,841
Chari 56.7
0.2
-
0.2
24.5
15.1
-
-
3.4 621
32
Exploring Kenya’s Inequality
A PUBLICATION OF KNBS AND SID
Cherab 50.7
0.2
-
0.5
26.7
13.4
0.1
-
8.5 1,951
Ngare Mara 36.6
0.7
0.1
12.3
31.9
1.3
0.3
6.7
10.0 673
Burat 63.6
0.8
-
1.7
23.4
1.6
1.3
2.8
4.9 2,206
Oldonyiro 12.0 -
-
1.2
6.0
8.0
16.2
25.6
31.0 1,437
Isiolo South Constituency 41.5
0.8
-
0.2
45.7
10.3
0.1
0.0
1.3 5,958
Garbatulla 29.3
1.4
-
0.0
55.4
12.1
0.1
0.0
1.7 2,397
Kinna 43.3
0.2
-
0.2
48.7
7.2
0.2
-
0.4 1,952
Sericho 57.7
0.7
-
0.4
27.8
11.3
0.1
-
2.1 1,609
Table 9.19: Main Roofing Material in Female Headed Households by County, Constituency and Wards
County/Constitu-ency/
Wards
Corrugated Iron Sheets
Tiles Concrete Asbestos sheets Grass Makuti Tin Mud/Dung
Other Households
Kenya 74.5 2.0
3.0 2.2
12.7 3.2
0.3 1.2
1.0 2,731,060
Rural 72.5 0.7
0.1 1.8
17.8 3.9
0.3 1.8
1.1 1,826,263
Urban 78.6 4.5
8.7 2.9
2.3 1.6
0.3 0.1
0.9 904,797
Isiolo County 60.9 0.5
0.3 1.2
18.4
5.8 2.4
4.8 5.9 11,857 Isiolo North Constit-uency 62.3 0.5
0.4 1.5
13.6
4.9 3.1
6.2 7.6 9,087
Wabera 91.6 2.3
1.5 3.2
1.2
- 0.1
- 0.1 1,314
BulaPesa 97.0 0.2
0.5 2.1
0.1
0.1 0.0
- - 2,304
Chari 66.5 0.2 - -
18.4
14.6 -
- 0.2 403
Cherab 62.1 0.2 - 0.2
23.4
10.7 -
0.1 3.3 1,305
Ngare Mara 15.4 - - 3.0
54.3
1.7 -
11.5 13.9 460
Burat 60.6 0.2 - 1.9
27.1
1.5 1.2
4.2 3.2 1,616
Oldonyiro 5.6 -
0.1 0.1
8.8
12.3 15.3
26.2 31.5 1,685 Isiolo South Constit-uency 56.1 0.7 - 0.0
34.1
8.7 -
0.3 0.1 2,770
Garbatulla 44.3 1.3 - -
42.6
11.3 -
0.4 0.1 1,076
Kinna 57.7 - - -
34.1
7.8 -
0.2 0.1 956
Sericho 71.1 0.8 - 0.1
21.7
6.0 -
0.1 0.1 738
33
Pulling Apart or Pooling Together?
Table 9.20: Main material of the wall by County, Constituency and Wards
County/Constituency/
Wards
Stone Brick/Block Mud/
Wood
Mud/
Cement
Wood only
Corrugated Iron Sheets
Grass/
Reeds
Tin Other Households
Kenya 16.7 16.9 36.5 7.7 11.1 6.7 3.0 0.3 1.2 8,493,380
Rural 5.7 13.8 50.0 7.6 14.4 2.5 4.4 0.3 1.4 5,239,879
Urban 34.5 21.9 14.8 7.8 5.8 13.3 0.8 0.3 0.9 3,253,501
Isiolo County 10.3 6.7 31.1 3.7 22.3 1.2 17.7 4.9 2.1 30,877
Isiolo North Constituency 14.2 8.3 28.1 2.1 29.6 1.5 6.8 6.8 2.7 22,149
Wabera 18.1 21.9 3.0 0.7 53.0 2.4 0.7 0.1 0.1
3,647
BulaPesa 35.3 8.4 6.4 0.6 47.6 1.5 0.1 0.1 0.1
6,145
Chari 0.3 0.6 61.1 1.9 3.8 0.3 26.7 0.1 5.3
1,024
Cherab 0.4 1.3 53.5 1.2 13.1 0.2 26.9 0.0 3.5
3,256
Ngare Mara 10.4 9.4 58.1 4.0 3.3 10.1 3.0 0.2 1.6
1,133
Burat 4.4 7.2 45.8 4.8 30.4 0.4 2.3 2.3 2.4
3,822
Oldonyiro 0.2 2.8 30.2 3.9 1.1 0.3 6.6 45.0 10.0
3,122
Isiolo South Constituency 0.5 2.6 38.8 7.7 3.8 0.6 45.2 0.1 0.7
8,728
Garbatulla 0.4 3.8 25.0 7.5 2.4 0.5 59.6 0.0 0.8
3,473
Kinna 0.4 2.1 42.7 5.1 3.7 0.8 44.8 0.1 0.3
2,908
Sericho 1.0 1.4 54.5 11.0 6.1 0.5 24.1 0.1 1.2
2,347
Table 9.21: Main Material of the Wall in Male Headed Households by County, Constituency and Ward
County/ Constit-uency/ Wards Stone
Brick/
Block
Mud/
Wood
Mud/
CementWood only Corrugated Iron
Sheets
Grass
/Reeds
TinOther Households
Kenya 17.5 16.6 34.7 7.6 11.4 7.4 3.4 0.3 1.2 5,762,320
Rural 5.8 13.1 48.9 7.3 15.4 2.6 5.2 0.3 1.4 3,413,616
Urban 34.6 21.6 14.0 7.9 5.6 14.4 0.7 0.3 0.9 2,348,704
Isiolo County 11.0
7.3
28.2
3.6 22.5 1.5
20.2
3.7 2.1 19,020
Isiolo North Constituency 15.7
9.5
25.4
1.9 30.6 1.9
7.1
5.3 2.5
13,062
Wabera 18.6
23.4
3.3
0.6 50.8 2.7
0.5
0.0 0.0
2,333
BulaPesa 36.9
8.3
6.1
0.5 46.6 1.4
0.1
0.1 0.1
3,841
Chari 0.3
0.6
58.3
1.3 3.2 0.2
30.0
0.2 6.0
621
Cherab 0.4
1.6
48.4
1.2 15.1 0.2
29.1 - 4.2
1,951
Ngare Mara 14.6
14.0
44.6
3.0 3.6 15.6
2.2
0.3 2.2
673
Burat 4.1
8.3
44.2
4.8 30.4 0.5
2.6
2.2 2.8
2,206
34
Exploring Kenya’s Inequality
A PUBLICATION OF KNBS AND SID
Oldonyiro 0.2
4.5
29.8
4.2 1.3 0.6
5.7
44.3 9.3
1,437 Isiolo South Constituency 0.7
2.4
34.4
7.2 4.5 0.8
49.0
0.1 1.0
5,958
Garbatulla 0.5
3.3
21.1
6.4 3.0 0.7
63.9
0.0 1.0
2,397
Kinna 0.3
2.2
37.9
4.8 4.1 1.1
49.1
0.1 0.4
1,952
Sericho 1.4
1.4
49.9
11.1 7.2 0.6
26.7
0.1 1.7
1,609
Table 9.22: Main Material of the Wall in Female Headed Households by County, Constituency andWard
County/ Constit-uency
Stone Brick/Block Mud/Wood Mud/Cement Wood only
Corrugated Iron Sheets
Grass/Reeds
Tin Other Households
Kenya 15.0 17.5 40.4 7.9
10.5 5.1
2.1
0.3
1.2 2,731,060
Rural 5.4 14.9 52.1 8.0 12.6 2.4
2.8
0.4
1.4 1,826,263
Urban 34.2 22.6 16.9 7.6
6.2 10.5
0.8
0.3
0.9 904,797
Isiolo County 9.2 5.6 35.8 3.9 22.1 0.7
13.6
6.8
2.3 11,857
Isiolo North Con-stituency
12.0 6.5 32.0 2.4
28.1 0.9
6.4
8.9
2.9 9,087
Wabera 17.2 19.3 2.5 0.9
56.8 1.9
1.1
0.1
0.1 1,314
BulaPesa 32.6 8.5 7.0 0.8
49.2 1.6
0.1
0.1
0.1 2,304
Chari 0.2 0.5 65.5 2.7 4.7 0.5
21.6
-
4.2 403
Cherab 0.5 0.8 61.1 1.1 10.1 0.2
23.8
-
2.5 1,305
Ngare Mara 4.3 2.8 77.8 5.4 2.8 2.0
4.1
-
0.7 460
Burat 4.9 5.7 47.9 4.7 30.4 0.3
1.9
2.4
1.9 1,616
Oldonyiro 0.2 1.3 30.6 3.6 1.0 0.1
7.3
45.5
10.5 1,685
Isiolo South Con-stituency 0.3 2.9 48.4 8.7
2.3 0.1
36.9
0.1
0.2 2,770
Garbatulla 0.2 4.8 33.6 9.9 0.9 0.2
50.1
-
0.3 1,076
Kinna 0.5 1.9 52.7 5.8 2.8 0.1
36.1
-
0.1 956
Sericho 0.3 1.4 64.6 10.8 3.8 0.1
18.6
0.3
0.1 738
35
Pulling Apart or Pooling Together?
Tabl
e 9.23
: Main
Mat
erial
of t
he W
all in
Fem
ale H
eade
d Ho
useh
olds
by C
ount
y, Co
nstit
uenc
y and
War
d
Coun
ty/Co
nstit-
uenc
y
/War
ds
Pond
Dam
Lake
Stre
am/
Rive
r
Unpr
otecte
d Sp
ring
Unpr
o-tec
ted
well
Jabia
Wate
r ven
dor
Othe
rUn
impr
oved
So
urce
sPr
otecte
d Sp
ring
Pro-
tected
W
ell
Bore
-ho
lePi
ped
into
Dwell
-ing
Pipe
dRa
in W
ater
Colle
ction
Im-
prov
ed
Sour
ces
No of
Indiv
id-ua
ls
Keny
a2.7
2.41.2
23.2
5.06.9
0.35.2
0.447
.47.6
7.711
.65.9
19.2
0.752
.6
37,9
19,64
7
Rura
l3.6
3.21.5
29.6
6.48.7
0.42.2
0.556
.09.2
8.112
.01.8
12.1
0.844
.0
26,0
75,19
5
Urba
n0.9
0.70.5
9.21.9
2.90.2
11.8
0.128
.34.0
6.810
.714
.734
.90.5
71.7
1
1,844
,452
Isiolo
Cou
nty0.1
0.50.0
11.7
2.022
.70.2
3.00.5
40.8
0.41.3
9.86.2
41.5
0.059
.2
1
39,39
6 Isi
olo N
orth
Co
nstitu
ency
0.10.0
0.010
.31.7
23.3
0.14.2
0.340
.20.6
1.16.7
8.343
.10.0
59.8
96
,699
Wab
era
0.10.0
0.00.0
0.10.6
0.110
.01.1
12.0
0.00.0
0.825
.961
.20.1
88.0
16
,264
BulaP
esa
0.10.0
0.15.9
0.10.0
0.15.9
0.612
.80.2
0.00.5
10.5
75.9
0.087
.2
22,20
3
Char
i0.0
0.00.0
14.6
0.03.9
0.05.2
0.023
.70.2
0.00.0
0.176
.00.0
76.3
4
,773
Cher
ab0.0
0.00.0
0.10.3
47.4
0.01.2
0.049
.00.1
0.48.9
3.538
.10.1
51.0
15
,475
Ngar
e Mar
a0.4
0.00.0
3.26.9
16.1
0.00.0
0.026
.77.3
17.5
46.6
0.11.7
0.073
.3
4,86
2
Bura
t0.1
0.00.0
33.5
5.89.9
0.13.9
0.053
.40.7
0.214
.45.3
25.9
0.146
.6
17,91
6
Oldo
nyiro
0.30.0
0.011
.81.4
81.5
0.00.0
0.095
.20.0
0.70.3
0.03.8
0.04.8
15
,206
Isiolo
Sou
th
Cons
tituen
cy0.1
1.70.0
14.8
2.721
.10.5
0.40.9
42.2
0.11.6
16.8
1.537
.80.0
57.8
42
,697
Garb
atulla
0.20.4
0.013
.13.6
23.8
0.00.1
2.443
.50.1
3.813
.42.0
37.2
0.056
.5
16,12
0
Kinn
a0.1
0.20.0
26.7
3.31.9
0.00.3
0.032
.50.1
0.029
.91.1
36.3
0.067
.5
14,55
1
Seric
ho0.0
5.40.0
2.60.8
40.8
1.60.8
0.052
.00.1
0.75.5
1.240
.50.0
48.0
12
,026
36
Exploring Kenya’s Inequality
A PUBLICATION OF KNBS AND SID
Tabl
e 9.24
: Sou
rce o
f Wat
er o
f Male
hea
ded
Hous
ehol
d by
Cou
nty C
onst
ituen
cy an
d W
ard
Cou
nty/C
onsti
tuenc
y/W
ards
Pond
Dam
Lake
Stre
am/
Rive
r
Unpr
otecte
d
Sprin
g
Unpr
otecte
d W
ellJa
biaW
ater
vend
orOt
her
Unim
prov
ed
Sour
ces
Prote
cted
Sprin
gPr
otecte
d W
ellBo
reho
lePi
ped i
nto
Dwell
ingPi
ped
Rain
Wate
r Co
llecti
on
Impr
oved
So
urce
sNo
. of
Indivi
duals
Keny
a
2.7
2.3
1.1
22.4
4.
8
6.7
0.4
5.6
0.4
46.4
7.4
7.7
11
.7
6.2
19
.9
0.7
53.6
26
,755,0
66
Rura
l
3.7
3.1
1.4
29.1
6.
3
8.6
0.4
2.4
0.5
55.6
9.2
8.2
12
.1
1.9
12
.2
0.8
44.4
18
,016,4
71
Urba
n
0.8
0.6
0.5
8.5
1.8
2.
8
0.2
12.1
0.1
27.5
3.8
6.7
10
.8
1
4.9
35
.8
0.5
72.5
8,738
,595
Isiolo
Cou
nty
0.1
0.5
0.0
12.5
2.
1
2
2.2
0.2
2.9
0.5
41.0
0.4
1.3
10
.1
6.5
40
.6
0.0
59.0
89
,044
Isiolo
Nor
th C
onsti
t-ue
ncy
0.1
0.0
0.0
10
.3
1.6
22.4
0.1
4.2
0.3
3
9.1
0.
6
1.0
7.0
9.2
43
.1
0.0
60.9
58
,689
Wab
era
0.1
-
-
0.0
0.
1
0.9
0.0
10
.3
1.0
1
2.4
-
0.0
0.8
2
7.1
59
.7
-
87
.6
10,33
0
BulaP
esa
0.1
0.1
-
5.9
0.1
0.
0
0.1
5.4
0.6
1
2.4
0.
2
0.0
0.5
1
1.6
75
.1
0.1
87.6
14
,314
Char
i
-
-
-
16.0
-
5.1
-
7.1
-
28.1
0.1
-
-
-
71.8
-
71.9
3,018
Cher
ab
-
0.1
-
0.1
0.
3
5
1.1
-
0.8
-
52.4
0.2
0.4
11
.7
3.4
31
.8
0.1
47.6
9,911
Ngar
e Mar
a
0.3
-
-
3.5
6.7
16.0
-
0.1
-
2
6.5
9.
8
16.0
44
.5
0.3
3.0
-
73.5
2,695
Bura
t
0.2
-
0.0
34
.6
5.8
9.
6
0.2
3.2
-
5
3.5
0.
4
0.3
14.7
5.
6
25.4
0.1
46
.5
10,74
1
Oldo
nyiro
0.4
-
-
11
.4
1.1
83.0
-
0.0
-
9
6.0
-
0.6
0.2
-
3.2
-
4.0
7,6
80
Isiolo
Sou
th C
onsti
t-ue
ncy
0.1
1.5
-
16.9
3.
1
2
1.7
0.4
0.3
0.8
44.8
0.1
1.9
16
.2
1.2
35
.7
0.0
55.2
30
,355
Garb
atulla
0.0
0.4
-
15.4
4.
1
2
5.2
-
0.1
2.2
47.3
0.1
4.5
14
.1
1.6
32
.3
-
52
.7
11,63
3
Kinn
a
0.1
0.1
-
30
.5
3.6
2.
1
-
0.2
-
3
6.6
0.
2
0.1
27.9
0.
8
34.5
-
63.4
10
,164
Seric
ho
0.0
4.8
-
3.0
1.
1
4
0.1
1.3
0.8
-
51.2
0.1
0.7
5.2
1.
1
41.6
0.0
48
.8
8,5
58
37
Pulling Apart or Pooling Together?
Tabl
e 9.25
: Sou
rce o
f Wat
er o
f Fem
ale h
eade
d Ho
useh
old
by C
ount
y, C
onst
ituen
cy, a
nd W
ard
Cou
nty/C
onsti
tuenc
y/
War
ds
Pond
Dam
Lake
Stre
am
Rive
r
Unpr
otecte
d Sp
ring
Unpr
otect-
ed W
ellJa
biaW
ater
vend
orOt
her
Unim
-pr
oved
So
urce
s
Pro-
tected
Sp
ring
Prote
ct-ed
Well
Bore
-ho
lePi
ped i
nto
Dwell
ingPi
ped
Rain
Wate
r Co
llec-
tion
Im-
prov
ed
Sour
c-es
No. o
f Ind
ividu
als
Keny
a
2.8
2
.7
1.3
25
.2
5.3
7.4
0.
3
4.4
0
.3
49.7
8.1
7.7
11.3
5.1
17.5
0.7
50
.3
11,16
4,581
Rura
l
3.4
3
.5
1.6
30
.6
6.5
8.9
0.
3
1.8
0
.4
57.0
9.5
8.0
11.5
1.6
11.7
0.8
43
.0
8,0
58,72
4
Urba
n
1.0
0
.8
0.6
11
.1
2.3
3.4
0.
2
11.1
0
.1
30.5
4.7
7.0
10.5
1
4.2
32.5
0.6
69
.5
3,1
05,85
7
Isiolo
Cou
nty
0.2
0
.5
0.0
10
.2
1.9
23.5
0.2
3
.2
0.5
40
.3
0.
4
1.2
9.2
5.8
43.0
0.0
59
.7
50,35
2
Isiolo
Nor
th C
onsti
tuenc
y
0.1
-
0.0
10
.4
1.9
24.8
0.0
4
.2
0.4
41
.8
0.
5
1.4
6.3
6.9
43.0
0.0
58
.2
38,01
0
Wab
era
0
.2
-
-
0
.0
0.0
0.1
0.
2
9.5
1
.3
11.2
-
-
0.8
2
3.9
63.9
0.2
88
.8
5,934
BulaP
esa
0
.1
-
0.2
5.8
0.1
-
-
6
.7
0.7
13
.6
0.
2
0.1
0.4
8.4
77.3
-
86.4
7,8
89
Char
i
-
-
-
12.1
-
1
.9
-
2
.1
-
16
.1
0.
3
-
-
0.2
8
3.3
-
83
.9
1,755
Cher
ab
0.1
-
-
-
0.2
4
0.8
-
1.8
-
42.9
-
0.4
3.8
3.7
4
9.2
-
57
.1
5,564
Ngar
e Mar
a
0.5
-
-
2.9
7.2
1
6.2
-
-
-
26.9
4.2
1
9.5
49.3
-
0.2
-
73.1
2,1
67
Bura
t
-
-
-
31.8
5.8
1
0.3
0.
1
5.0
-
53.1
1.0
0.1
14.1
4.8
26.8
0.1
46
.9
7,175
Oldo
nyiro
0
.2
-
-
12
.3
1.8
79.9
-
0
.1
-
94
.3
0.
1
0.8
0.4
-
4.5
-
5.7
7,526
Isiolo
Sou
th C
onsti
tuenc
y
0.2
2
.2
-
9.5
1.8
1
9.8
0.
7
0.4
1
.1
35.6
-
0.9
18.3
2.1
43.1
0.0
64
.4
12,34
2
Garb
atulla
0
.5
0.5
-
7
.2
2.3
20.1
0.1
-
3.0
33
.7
-
2.0
11
.4
3.2
4
9.7
0.1
66.3
4,4
87
Kinn
a
-
0
.3
-
18.1
2.6
1
.5
-
0
.6
-
23
.0
-
-
34.7
1.7
40.5
-
77.0
4,3
87
Seric
ho
0.1
6
.8
-
1.5
0.1
4
2.5
2.
4
0.7
-
54.1
-
0.5
6.3
1.3
3
7.7
-
45
.9
3,468
38
Exploring Kenya’s Inequality
A PUBLICATION OF KNBS AND SID
Table 9.26: Human Waste Disposal by County, Constituency and Ward
County/ Constit-uency
Main Sewer Septic Tank
Cess Pool
VIP Latrine
Pit Latrine
Improved Sanitation
Pit Latrine Uncovered
Bucket Bush Other Unim-proved Sanitation
Number of HH Memmbers
Kenya 5.91 2.76 0.27 4.57 47.62 61.14 20.87 0.27 17.58 0.14 38.86 37,919,647 Rural 0.14 0.37 0.08 3.97 48.91 53.47 22.32 0.07 24.01 0.13 46.53 26,075,195 Urban 18.61 8.01 0.70 5.90 44.80 78.02 17.67 0.71 3.42 0.18 21.98 11,844,452 Isiolo County 4.14 1.26 0.05 2.63 32.02 40.11 12.91 0.11 46.52 0.36 59.89 139,396 Isiolo North Constituency 5.90 1.79 0.05 2.24 33.06 43.04 15.62 0.11 41.01 0.22 56.96 96,699 Wabera 22.28 3.90 0.10 2.53 17.79 46.61 47.07 0.20 5.20 0.92 53.39 16,264 BulaPesa 8.73 2.51 0.08 2.77 66.60 80.69 17.06 0.20 1.98 0.07 19.31 22,203 Chari 0.04 0.04 0.10 1.47 38.93 40.58 11.86 0.08 47.22 0.25 59.42 4,773 Cherab 0.10 0.02 0.00 1.89 36.22 38.23 6.66 0.00 55.11 0.00 61.77 15,475 Ngare Mara 1.21 6.48 0.00 1.71 16.15 25.55 0.35 0.08 73.88 0.14 74.45 4,862 Burat 0.36 1.11 0.02 2.43 33.30 37.21 8.82 0.08 53.80 0.09 62.79 17,916 Oldonyiro 0.00 0.15 0.05 1.68 0.49 2.37 3.08 0.07 94.44 0.05 97.63 15,206 Isiolo South Constituency 0.17 0.06 0.06 3.52 29.67 33.48 6.77 0.09 58.99 0.68 66.52 42,697 Garbatulla 0.00 0.15 0.11 5.33 21.25 26.84 2.35 0.02 70.79 0.00 73.16 16,120 Kinna 0.05 0.02 0.03 3.36 27.88 31.34 7.59 0.18 59.89 1.00 68.66 14,551 Sericho 0.53 0.00 0.04 1.27 43.11 44.95 11.69 0.07 42.08 1.20 55.05 12,026
Table 9.27:Human Waste Disposal in Male Headed household by County, Constituency and Ward
County/ Constituency/wards
Main Sewer
Septic Tank
Cess Pool
VIP La-trine
Pit Latrine
Improved Sanitation
Pit Latrine Uncov-ered
Bucket Bush Other Unimproved Sanitation
Number of HH Memmbers
Kenya 6.30 2.98 0.29 4.60 47.65 61.81 20.65 0.28 17.12 0.14 38.19 26,755,066
Rural 0.15 0.40 0.08 3.97 49.08 53.68 22.22 0.07 23.91 0.12 46.32 18,016,471
Urban 18.98 8.29 0.73 5.89 44.69 78.58 17.41 0.70 3.13 0.18 21.42 8,738,595
Isiolo County 4.22 1.40 0.07 2.61 31.44 39.74 12.53 0.10 47.24 0.38 60.26 89,044
Isiolo North Constituency 6.31 2.09 0.06 2.34 33.67 44.48 16.00 0.12 39.18 0.21 55.52 58,689
Wabera 22.18 4.09 0.10 2.83 18.03 47.22 46.65 0.27 5.13 0.73 52.78 10,330
BulaPesa 9.12 2.61 0.08 2.49 66.27 80.56 17.38 0.18 1.77 0.10 19.44 14,314
Chari 0.00 0.07 0.17 2.15 36.35 38.73 9.05 0.00 51.82 0.40 61.27 3,018
Cherab 0.09 0.01 0.00 1.77 32.78 34.65 5.96 0.00 59.39 0.00 65.35 9,911
Ngare Mara 1.93 9.80 0.00 1.52 17.92 31.17 0.30 0.00 68.27 0.26 68.83 2,695
39
Pulling Apart or Pooling Together?
Burat 0.43 1.40 0.04 2.60 33.05 37.51 8.73 0.12 53.49 0.15 62.49 10,741
Oldonyiro 0.00 0.21 0.09 2.17 0.42 2.89 3.59 0.05 93.46 0.00 97.11 7,680
Isiolo South Constituency 0.17 0.07 0.07 3.13 27.14 30.59 5.82 0.07 62.82 0.71 69.41 30,355
Garbatulla 0.00 0.15 0.15 4.70 18.29 23.30 2.14 0.02 74.55 0.00 76.70 11,633
Kinna 0.00 0.03 0.00 2.82 26.55 29.41 6.16 0.18 63.13 1.12 70.59 10,164
Sericho 0.62 0.00 0.06 1.37 39.86 41.90 10.41 0.00 46.51 1.18 58.10 8,558
Table 9.28: Human Waste Disposal in Female Headed Household by County, Constituency and Ward
County/ Con-stituency
Main Sewer
Septic Tank
Cess Pool
VIP Latrine Pit Latrine
Improved Sanita-tion
Pit Latrine Uncov-ered Bucket Bush Other
Unim-proved Sanita-tion
Number of HH Memmbers
Kenya 5.0 2.2 0.2 4.5 47.6 59.5 21.4 0.3 18.7 0.2 40.5 11,164,581.0
Rural 0.1 0.3 0.1 4.0 48.5 53.0 22.6 0.1 24.2 0.1 47.0 8,058,724.0
Urban 17.6 7.2 0.6 5.9 45.1 76.4 18.4 0.7 4.3 0.2 23.6 3,105,857.0
Isiolo 4.0 1.0 0.0 2.7 33.0 40.8 13.6 0.1 45.2 0.3 59.2 50,352.0
Isiolo North 5.3 1.3 0.0 2.1 32.1 40.8 15.0 0.1 43.8 0.2 59.2 38,010.0
Wabera 22.5 3.6 0.1 2.0 17.4 45.6 47.8 0.1 5.3 1.3 54.4 5,934.0
BulaPesa 8.0 2.3 0.1 3.3 67.2 80.9 16.5 0.2 2.3 0.0 19.1 7,889.0
Chari 0.1 0.0 0.0 0.3 43.4 43.8 16.7 0.2 39.3 0.0 56.2 1,755.0
Cherab 0.1 0.0 0.0 2.1 42.3 44.6 7.9 0.0 47.5 0.0 55.4 5,564.0
Ngare Mara 0.3 2.4 0.0 1.9 13.9 18.6 0.4 0.2 80.8 0.0 81.4 2,167.0
Burat 0.3 0.7 0.0 2.2 33.7 36.8 8.9 0.0 54.3 0.0 63.2 7,175.0
Oldonyiro 0.0 0.1 0.0 1.2 0.6 1.8 2.6 0.1 95.4 0.1 98.2 7,526.0
Isiolo South 0.2 0.0 0.0 4.5 35.9 40.6 9.1 0.1 49.6 0.6 59.4 12,342.0
Garbatulla 0.0 0.1 0.0 7.0 28.9 36.0 2.9 0.0 61.0 0.0 64.0 4,487.0
Kinna 0.2 0.0 0.1 4.6 31.0 35.8 10.9 0.2 52.4 0.7 64.2 4,387.0
Sericho 0.3 0.0 0.0 1.0 51.1 52.5 14.9 0.3 31.2 1.2 47.5 3,468.0